precrec: precrec: A package for computing accurate ROC and...

precrecR Documentation

precrec: A package for computing accurate ROC and Precision-Recall curves


The precrec package contains several functions and S3 generics to provide a robust platform for performance evaluation of binary classifiers.


The precrec package provides the following six functions.

Function Description
evalmod Main function to calculate evaluation measures
mmdata Reformat input data for performance evaluation calculation
join_scores Join scores of multiple models into a list
join_labels Join observed labels of multiple test datasets into a list
create_sim_samples Create random samples for simulations
format_nfold Create n-fold cross validation dataset from data frame

S3 generics

The precrec package provides nine different S3 generics for the S3 objects generated by the evalmod function.

S3 generic Library Description
print base Print the calculation results and the summary of the test data base Convert a precrec object to a data frame
plot graphics Plot performance evaluation measures
autoplot ggplot2 Plot performance evaluation measures with ggplot2
fortify ggplot2 Prepare a data frame for ggplot2
auc precrec Make a data frame with AUC scores
part precrec Calculate partial curves and partial AUC scores
pauc precrec Make a data frame with pAUC scores
auc_ci precrec Calculate confidence intervals of AUC scores

Performance measure calculations

The evalmod function calculates ROC and Precision-Recall curves and returns an S3 object. The generated S3 object can be used with several different S3 generics, such as print and plot. The evalmod function can also calculate basic evaluation measures - error rate, accuracy, specificity, sensitivity, precision, Matthews correlation coefficient, and F-Score.

Data preparation

The mmdata function creates an input dataset for the evalmod function. The generated dataset contains formatted scores and labels.

join_scores and join_labels are helper functions to combine multiple scores and labels.

The create_sim_samples function creates test datasets with five different performance levels.

Data visualization

plot takes an S3 object generated by evalmod as input and plot corresponding curves.

autoplot uses ggplot to plot curves.

Result retrieval takes an S3 object generated by evalmod as input and and returns a data frame with calculated curve points.

auc and pauc returns a data frame with AUC scores and partial AUC scores, respectively. auc_ci returns confidence intervals of AUCs for both ROC and precision-recall curves.

precrec documentation built on Oct. 12, 2023, 1:06 a.m.