Package index

Package

precrec-package precrec

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

Main

evalmod()

Evaluate models and calculate performance evaluation measures

metric_curve()

Draw one evaluation measure against another

Data preparation

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

format_nfold()

Create n-fold cross validation dataset from data frame

create_sim_samples()

Create random samples for simulations

Visualization

plot(<sscurves>) plot(<mscurves>) plot(<smcurves>) plot(<mmcurves>) plot(<sspoints>) plot(<mspoints>) plot(<smpoints>) plot(<mmpoints>) plot(<ssxycurves>) plot(<msxycurves>) plot(<smxycurves>) plot(<mmxycurves>)

Plot performance evaluation measures

autoplot(<sscurves>) autoplot(<mscurves>) autoplot(<smcurves>) autoplot(<mmcurves>) autoplot(<sspoints>) autoplot(<mspoints>) autoplot(<smpoints>) autoplot(<mmpoints>) autoplot(<ssxycurves>) autoplot(<msxycurves>) autoplot(<smxycurves>) autoplot(<mmxycurves>)

Plot performance evaluation measures with ggplot2

fortify(<sscurves>) fortify(<mscurves>) fortify(<smcurves>) fortify(<mmcurves>) fortify(<sspoints>) fortify(<mspoints>) fortify(<smpoints>) fortify(<mmpoints>) fortify(<ssxycurves>) fortify(<msxycurves>) fortify(<smxycurves>) fortify(<mmxycurves>)

Convert a curves and points object to a data frame for ggplot2

Data retrieval

as.data.frame(<sscurves>) as.data.frame(<mscurves>) as.data.frame(<smcurves>) as.data.frame(<mmcurves>) as.data.frame(<sspoints>) as.data.frame(<mspoints>) as.data.frame(<smpoints>) as.data.frame(<mmpoints>) as.data.frame(<aucroc>) as.data.frame(<ssxycurves>) as.data.frame(<msxycurves>) as.data.frame(<smxycurves>) as.data.frame(<mmxycurves>)

Convert a curves and points object to a data frame

as.data.table(<sscurves>) as.data.table(<mscurves>) as.data.table(<smcurves>) as.data.table(<mmcurves>) as.data.table(<sspoints>) as.data.table(<mspoints>) as.data.table(<smpoints>) as.data.table(<mmpoints>) as.data.table(<aucroc>)

Convert a curves and points object to a data.table

auc()

Retrieve a data frame of AUC scores

pauc()

Retrieve a data frame of pAUC scores

Partial AUC and partial curve

part()

Calculate partial AUCs

Confidence interval of AUC scores

auc_ci()

Calculate CIs of ROC and precision-recall AUCs

Precision-recall break-even point

prbe()

Calculate the precision-recall break-even point

Average precision

average_precision()

Calculate the average precision

Probability-based metrics

prob_metrics()

Calculate the Brier score, the RMSE and the log loss of predicted probabilities

prob_metrics_ci()

Calculate CIs of the Brier score and the log loss

Datasets

P10N10, B500, B1000, IB500, IB1000, M2N50F5 and C3N150

P10N10

A small example dataset with several tied scores.

B500

Balanced data with 500 positives and 500 negatives.

B1000

Balanced data with 1000 positives and 1000 negatives.

IB500

Imbalanced data with 500 positives and 5000 negatives.

IB1000

Imbalanced data with 1000 positives and 10000 negatives.

M2N50F5

5-fold cross validation sample.

C3N150

Multiclass sample with three classes.