ROCnReg (version 1.0-8)

plot.cROC: Default cROC plotting

Description

Takes a fitted cROC object produced by cROC.bnp(), cROC.sp(), or cROC.kernel() and plots the covariate-specific ROC curve (cROC) and associated area under the cROC curve. The suitable type of graphic is chosen according to the number and nature of the covariates.

Usage

# S3 method for cROC
plot(x, ask = TRUE, ...)

Arguments

x

An object of class cROC as produced by cROC.bnp(), cROC.sp(), or cROC.kernel().

ask

A logical value. If TRUE, the default, the user is asked for confirmation, before a new figure is drawn.

...

Further arguments passed to or from other methods.

See Also

cROC.bnp, cROC.sp or cROC.kernel

Examples

library(ROCnReg)
data(psa)
# Select the last measurement
newpsa <- psa[!duplicated(psa$id, fromLast = TRUE),]

# Log-transform the biomarker
newpsa$l_marker1 <- log(newpsa$marker1)
# \donttest{
cROC_bnp <- cROC.bnp(formula.h = l_marker1 ~ f(age, K = 0),
               formula.d = l_marker1 ~ f(age, K = 0),
               group = "status", 
               tag.h = 0,
               data = newpsa,
               standardise = TRUE, 
               p = seq(0, 1, len = 101),
               compute.lpml = TRUE, 
               compute.WAIC = TRUE,
               compute.DIC = TRUE, 
               pauc = pauccontrol(compute = TRUE, value = 0.5, focus = "FPF"),
               density = densitycontrol(compute = TRUE, grid.h = NA, grid.d = NA),
               mcmc = mcmccontrol(nsave = 500, nburn = 100, nskip = 1))

plot(cROC_bnp)
# }
# \dontshow{
agep <- seq(min(newpsa$age), max(newpsa$age), length = 5)
df.pred <- data.frame(age = agep)
cROC_bnp <- cROC.bnp(formula.h = l_marker1 ~ f(age, K = 0),
               formula.d = l_marker1 ~ f(age, K = 0),
               group = "status", 
               tag.h = 0,
               data = newpsa,
               newdata = df.pred,
               standardise = TRUE, 
               p = seq(0, 1, len = 101),
               compute.lpml = FALSE, 
               compute.WAIC = FALSE,
               compute.DIC = FALSE, 
               pauc = pauccontrol(compute = FALSE, value = 0.5, focus = "FPF"),
               density = densitycontrol(compute = FALSE, grid.h = NA, grid.d = NA),
               mcmc = mcmccontrol(nsave = 50, nburn = 10, nskip = 1))

plot(cROC_bnp)
# }