pr_ci: PR bootstrap confidence intervals

In multiROC: Calculating and Visualizing ROC and PR Curves Across Multi-Class Classifications

Description

This function uses bootstrap to generate five types of equi-tailed two-sided confidence intervals of PR-AUC with different required percentages.

Usage

pr_ci(data, conf= 0.95, type='basic', R = 100, index = 4)

Arguments

data	A data frame contains true labels of multiple groups and corresponding predictive scores.
conf	A scalar contains the required level of confidence intervals, and the default number is 0.95.
type	A vector of character strings includes five different types of equi-tailed two-sided nonparametric confidence intervals (e.g., "norm","basic", "stud", "perc", "bca", "all").
R	A scalar contains the number of bootstrap replicates, and the default number is 100.
index	A scalar contains the position of the variable of interest.

Details

A data frame is required for this function as input. This data frame should contains true label (0 - Negative, 1 - Positive) columns named as XX_true (e.g. S1_true, S2_true and S3_true) and predictive scores (continuous) columns named as XX_pred_YY (e.g. S1_pred_SVM, S2_pred_RF). Predictive scores could be probabilities among [0, 1] and other continuous values. For each classifier, the number of columns should be equal to the number of groups of true labels. The order of columns won't affect results.

Value

norm	Using the normal approximation to calculate the confidence intervals.
basic	Using the basic bootstrap method to calculate the confidence intervals.
stud	Using the studentized bootstrap method to calculate the confidence intervals.
perc	Using the bootstrap percentile method to calculate the confidence intervals.
bca	Using the adjusted bootstrap percentile method to calculate the confidence intervals.
all	Using all previous bootstrap methods to calculate the confidence intervals.

Examples

## Not run: data(test_data)
pr_ci_res <- pr_ci(test_data, conf= 0.95, type='basic', R = 1000, index = 4)
## End(Not run)

multiROC documentation built on May 1, 2019, 10:11 p.m.