AROC (version 1.0-4)

pooledROC.BB: Bayesian bootstrap estimation of the pooled ROC curve.

Description

Estimates the pooled ROC curve using the Bayesian bootstrap estimator proposed by Gu et al. (2008).

Usage

pooledROC.BB(y0, y1, p = seq(0, 1, l = 101), B = 5000)

Arguments

y0

Diagnostic test outcomes in the healthy group.

y1

Diagnostic test outcomes in the diseased group.

p

Set of false positive fractions (FPF) at which to estimate the covariate-adjusted ROC curve.

B

An integer value specifying the number of Bayesian bootstrap resamples. By default 5000.

Value

As a result, the function provides a list with the following components:

call

the matched call.

p

Set of false positive fractions (FPF) at which the pooled ROC curve has been estimated

ROC

Estimated pooled ROC curve, and corresponding 95% credible intervals

AUC

Estimated pooled AUC, and corresponding 95% credible intervals.

References

Gu, J., Ghosal, S., and Roy, A. (2008). Bayesian bootstrap estimation of ROC curve. Statistics in Medicine, 27(26), 5407 - 5420.

See Also

AROC.bnp, AROC.bsp, AROC.sp, AROC.kernel, pooledROC.BB or pooledROC.emp.

Examples

# NOT RUN {
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)
# }
# NOT RUN {
m0_BB <- pooledROC.BB(newpsa$l_marker1[newpsa$status == 0],
newpsa$l_marker1[newpsa$status == 1], p = seq(0,1,l=101), B = 5000)

summary(m0_BB)

plot(m0_BB)
# }