Description Usage Arguments Details Value Examples
This function uses bootstrap to generate five types of equi-tailed two-sided confidence intervals of PR-AUC with different required percentages.
1 |
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. |
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.
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. |
1 2 3 |
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.