caROC (version 0.1.5)

plot_caROC: Plot covariate-adjusted ROC.

Description

Function to plot the ROC curve generated from caROC().

Usage

plot_caROC(myROC, ...)

Arguments

myROC

ROC output from caROC() function.

…

Arguments to tune generated plots.

Value

Plot the ROC curve.

Details

This function can be used to plot other ROC curve, as long as the input contains two components "sensitivity" and "specificity".

Examples

# NOT RUN {
n1 = n0 = 500

## 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)
userFormula = "M~Z"

ROC1 <- caROC(diseaseData,controlData,userFormula,
                 mono_resp_method = "none")
ROC2 <- caROC(diseaseData,controlData,userFormula,
                 mono_resp_method = "ROC")

plot_caROC(ROC1)
plot_caROC(ROC2, col = "blue")
# }