| Title: | Aggregate Multiple ROC Curves into One Global ROC |
| Version: | 1.0.1 |
| Description: | 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. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.1.1 |
| Imports: | utils, magrittr |
| Suggests: | testthat (≥ 3.0.0), mockery, mockr, knitr, rmarkdown, ROCR, pROC, pracma, stats |
| Config/testthat/edition: | 3 |
| VignetteBuilder: | knitr |
| URL: | https://gitlab.com/UM-CDS/general-tools/rocaggregator |
| BugReports: | https://gitlab.com/UM-CDS/general-tools/rocaggregator/-/issues |
| NeedsCompilation: | no |
| Packaged: | 2021-08-09 15:29:58 UTC; admin |
| Author: | Pedro Mateus 
[aut, cre] |
| Maintainer: | Pedro Mateus <pedro-cm@live.com.pt> |
| Repository: | CRAN |
| Date/Publication: | 2021-08-10 09:10:14 UTC |
Compute the global confusion matrix from the FPR and TPR obtained from each node
Description
Compute the global confusion matrix from the FPR and TPR obtained from each node
Usage
partial_cm(
fpr,
tpr,
thresholds,
negative_count,
total_count,
descending = FALSE
)
Arguments
fpr |
list - False positive rates for each individual ROC |
tpr |
list - True positive rates for each individual ROC |
thresholds |
list - Thresholds used to compute the fpr and tpr |
negative_count |
list - Total number of samples corresponding to the negative case |
total_count |
list - Total number of samples |
descending |
thresholds in descending order? |
Value
global confusion matrix and thresholds
Compute the precision recall curve
Description
Compute the precision recall curve
Usage
precision_recall_curve(fpr, tpr, thresholds, negative_count, total_count)
Arguments
fpr |
list - False positive rates for each individual ROC. |
tpr |
list - True positive rates for each individual ROC. |
thresholds |
list - Thresholds used to compute the fpr and tpr. |
negative_count |
vector - Total number of samples corresponding to the negative case. |
total_count |
vector - Total number of samples. |
Value
list with the global precision, recall, and thresholds (increasing)
Compute Receiver operating characteristic (ROC)
Description
Compute Receiver operating characteristic (ROC)
Usage
roc_curve(fpr, tpr, thresholds, negative_count, total_count)
Arguments
fpr |
list - False positive rates for each individual ROC |
tpr |
list - True positive rates for each individual ROC |
thresholds |
list - Thresholds used to compute the fpr and tpr |
negative_count |
vector - Total number of samples corresponding to the negative case |
total_count |
vector - Total number of samples |
Value
list with the global fpr, tpr, and thresholds (decreasing)
Shift a vector left or right according to the value provided
Description
Shift a vector left or right according to the value provided
Usage
shift_vector(x, n)
Arguments
x |
the vector |
n |
shift |
Value
the vector shifted
Examples
shift_vector(c(1,2,3,4), 1)
shift_vector(c(1,2,3,4), -1)