plot_caROC_CB {caROC} R Documentation

## Plot confidence band of covariate-adjusted ROC.

### Description

A function to plot the confidence band of covariate-adjusted ROC.

### Usage

plot_caROC_CB(myROC_CB, add = TRUE, ...)


### Arguments

 myROC_CB Output from caROC_CB() function. add Whether to add confidence band to existing plot (TRUE) or draw a new one (FALSE). Default is TRUE. ... Any parameters related with the plot.

### Value

No values will be return. This function is for plotting only.

### Author(s)

Ziyi Li<ziyi.li@emory.edu>

### Examples

library(caROC)
n1 = n0 = 100

## generate data
Z_D <- rbinom(n1, size = 1, prob = 0.3)
Z_C <- rbinom(n0, size = 1, prob = 0.7)

Y_C_Z0 <- rnorm(n0, 0.1, 1)
Y_D_Z0 <- rnorm(n1, 1.1, 1)
Y_C_Z1 <- rnorm(n0, 0.2, 1)
Y_D_Z1 <- rnorm(n1, 0.9, 1)

M0 <- Y_C_Z0 * (Z_C == 0) + Y_C_Z1 * (Z_C == 1)
M1 <- Y_D_Z0 * (Z_D == 0) + Y_D_Z1 * (Z_D == 1)

diseaseData <- data.frame(M = M1, Z = Z_D)
controlData <- data.frame(M = M0, Z = Z_C)
formula = "M~Z"

ROC_CB1 <- caROC_CB(diseaseData,controlData,formula,
mono_resp_method = "none",
CB_alpha = 0.95,
nbin = 100,verbose = FALSE)
### plot confidence band individually
plot_caROC_CB(ROC_CB1, add = FALSE, lty = 2, col = "blue")

### plot confidence band together with the ROC curve
ROC1 <- caROC(diseaseData,controlData,formula,
mono_resp_method = "none", verbose = FALSE)
plot_caROC(ROC1)
plot_caROC_CB(ROC_CB1, add = TRUE, lty = 2, col = "blue")


[Package caROC version 0.1.5 Index]