roc_plot | R Documentation |
A function to plot ROC curves. Note that the NA values in the data will be replaced with zero.
roc_plot(
cp,
ca,
group = NULL,
byDR = FALSE,
cumdata = FALSE,
grayscale = FALSE,
...
)
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. |
cumdata |
Whether to output the cumulative data that are used to create the ROC curves. Default 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 |
Plot ROC curves and calculate AUCs as side effects.
Yueran Yang & Andrew Smith. (2022). "fullROC: An R package for generating and analyzing eyewitness-lineup ROC curves." Behavior Research Methods. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.3758/s13428-022-01807-6")}
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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1177/1745691620902426")}
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)
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