paauccontrol: Partial area under the covariate-adjusted ROC curve (pAAUC).

In AROC: Covariate-Adjusted Receiver Operating Characteristic Curve Inference

Partial area under the covariate-adjusted ROC curve (pAAUC).

Description

Used to set various parameters controlling the estimation of the partial area under the covariate-adjusted ROC curve (pAAUC).

Usage

paauccontrol(compute = FALSE, value = 1)

Arguments

compute	Logical value. If TRUE the partial area under the covariate-adjusted ROC curve (pAAUC) is estimated.
value	Numeric value. Pre-specified maximum false positive fraction (FPF) at which to calculate the pAAUC.

Details

The value returned by this function is used as a control argument of the AROC.bnp and AROC.bsp functions.

Value

a list with components for each of the possible arguments.

References

Inacio de Carvalho, V., and Rodriguez-Alvarez, M. X. (2018). Bayesian nonparametric inference for the covariate-adjusted ROC curve. Technical report.

See Also

AROC.bnp and AROC.bsp

Examples

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

# Log-transform the biomarker
newpsa$l_marker1 <- log(newpsa$marker1)

m0 <- AROC.bnp(formula.healthy = l_marker1 ~ f(age, K = 0),
group = "status", tag.healthy = 0, data = newpsa, scale = TRUE,
p = seq(0,1,l=101), paauc = list(compute = TRUE, value = 0.3),
compute.lpml = TRUE, compute.WAIC = TRUE,
a = 2, b = 0.5, L = 10, nsim = 5000, nburn = 1000)

summary(m0)

AROC documentation built on March 18, 2022, 5:22 p.m.