Nonparametric bootstrap methods for interval estimation of the area under the ROC curve for correlated diagnostic test data: application to whole-virus ELISA testing in swine
This repository is a repository about the extended information for paper on Frontiers in Veterinary Science "Nonparametric bootstrap methods for interval estimation of the area under the ROC curve for correlated diagnostic test data: application to whole-virus ELISA testing in swine". It mainly contains code for calc_ci function which can calculate point estimate and confidence interval of AUC given a data, subject, specified formula, specified bootstrap method, specified bootstrap replicates, and specified confidence level .
Here we described two nonparametric bootstrap methods, which are the cluster bootstrap and hierarchical bootstrap. By combining these two functions, we created a single function calc_ci which can calculate point estimate and confidence interval of AUC using two bootstrap methods.
Software and R packages versions are listed here for better reproducibility.
sessionInfo() R version 4.2.2 (2022-10-31) Platform: aarch64-apple-darwin20 (64-bit) Running under: macOS Monterey 12.5
Matrix products: default LAPACK: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRlapack.dylib
locale: [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages: [1] parallel stats graphics grDevices utils datasets methods base
other attached packages:
[1] here_1.0.1 pROC_1.18.0 lme4_1.1-31 Matrix_1.5-1 boot_1.3-28 forcats_0.5.2
[7] stringr_1.4.1 dplyr_1.0.10 purrr_0.3.5 readr_2.1.3 tidyr_1.2.1 tibble_3.1.8
[13] ggplot2_3.4.0 tidyverse_1.3.2