roc_plot: A function to plot ROC curves.

In fullROC: Plot Full ROC Curves using Eyewitness Lineup Data

Description

A function to plot ROC curves.

Usage

roc_plot(cp, ca, group = NULL, byDR = FALSE, grayscale = FALSE, ...)

Arguments

cp	A vector of cp id rates or frequencies.
ca	A vector of ca id rates or frequencies.
group	Grouping variable to indicate group membership. Will create an ROC curve and calculate AUC for each group.
byDR	Whether to order ids by diagnosticity ratios. Defaults to FALSE.
grayscale	Whether to produce the plot in grayscale. Defaults to FALSE.
...	Additional plotting parameters. For example, users can change x-axis and y-axis labels using xlab and ylab.

Value

Plot ROC curves and calculate AUCs as side effects.

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)
roc_plot(cpf1, caf1)


cpf2 <- c(90, 40, 20)
caf2 <- c(10, 70, 80)
roc_plot(cpf2, caf2)

## plot two ROC curves
cpf <- c(cpf1, cpf2)
caf <- c(caf1, caf2)
group <- rep(letters[1:2], times = c(length(cpf1), length(cpf2) ) )
roc_plot(cpf, caf, group = group)

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