| auc_ci {fullROC} | R Documentation |
Bootstrap confidence intervals for AUC
Description
A function to simulate bootstrap samples and calculate CIs for AUC and differences.
Usage
auc_ci(cpf, caf, group = NULL, nboot = 1000, alpha = 0.05, ...)
Arguments
cpf |
A vector of cp frequencies. |
caf |
A vector of ca frequencies. |
group |
A vector indicating group membership. Will calculate AUC by group. |
nboot |
Number of bootstrap iterations. Defaults to 1,000. |
alpha |
Alpha level for the CIs. Defaults to 0.05. |
... |
Additional arguments in auc_boot. Will allow users to adjust ca id rates in each simulation. |
Value
A data frame of CIs for each group and group differences.
References
Yueran Yang & Andrew Smith. (2020). "fullROC: An R package for generating and analyzing eyewitness-lineup ROC curves" doi: 10.13140/RG.2.2.20415.94885/1
Andrew Smith, Yueran Yang, & Gary Wells. (2020). "Distinguishing between investigator discriminability and eyewitness discriminability: A method for creating full receiver operating characteristic curves of lineup identification performance". Perspectives on Psychological Science, 15(3), 589-607. doi: 10.1177/1745691620902426
Examples
cpf1 <- c(100, 90, 80, 20, 10, 5)
caf1 <- c(6, 7, 15, 50, 75, 120)
auc_ci(cpf1, caf1, nboot = 50)
cpf2 <- c(90, 40, 20)
caf2 <- c(10, 70, 80)
auc_ci(cpf2, caf2, nboot = 100)
## compare two groups
cpf <- c(cpf1, cpf2)
caf <- c(caf1, caf2)
group <- rep(letters[1:2], times = c(length(cpf1), length(cpf2) ) )
auc_ci(cpf, caf, group = group)