caROC (version 0.1.5)

plot_sscaROC: Plot covariate-adjusted ROC for specific subpopulations.

Description

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

Usage

plot_sscaROC(myROC, ...)

Arguments

myROC

ROC output from sscaROC() 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 = 1000

## generate data
Z_D1 <- rbinom(n1, size = 1, prob = 0.3)
Z_D2 <- rnorm(n1, 0.8, 1)

Z_C1 <- rbinom(n0, size = 1, prob = 0.7)
Z_C2 <- rnorm(n0, 0.8, 1)

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_C1 == 0) + Y_C_Z1 * (Z_C1 == 1) + Z_C2
M1 <- Y_D_Z0 * (Z_D1 == 0) + Y_D_Z1 * (Z_D1 == 1) + 1.5 * Z_D2

diseaseData <- data.frame(M = M1, Z1 = Z_D1, Z2 = Z_D2)
controlData <- data.frame(M = M0, Z1 = Z_C1, Z2 = Z_C2)
userFormula = "M~Z1+Z2"

target_covariates = c(1, 0.7, 0.9)

# }
# NOT RUN {
myROC <- sscaROC(diseaseData,
                 controlData,
                 userFormula,
                 target_covariates,
                 global_ROC_controlled_by = "sensitivity",
                 mono_resp_method = "none")
plot_sscaROC(myROC, lwd = 1.6)
# }