auc_ci: Bootstrap confidence intervals for AUC

In fullROC: Plot Full ROC Curves using Eyewitness Lineup Data

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)

fullROC documentation built on Jan. 16, 2021, 5:40 p.m.