logcondens (version 2.1.8)

confIntBootLogConROC_t0: Function to compute a bootstrap confidence interval for the ROC curve at a given t, based on the log-concave ROC curve

Description

This function computes a bootstrap confidence interval for the ROC curve at a given value false negative fraction (1 - specificity) \(t\). The ROC curve estimate is based on log-concave densities, as discussed in Rufibach (2011).

Usage

confIntBootLogConROC_t0(controls, cases, grid = c(0.2, 0.8), conf.level = 0.95, 
M = 1000, smooth = TRUE, output = TRUE)

Value

A list containing the following elements:

qs

data.frame with the columns t (false positive fractions where confidence interval is computed at) and the confidence intervals for the ROC curve at grid, based on the log-concave density estimate.

boot.mat

Bootstrap samples for the ROC curve based on the log-concave density estimate.

qs.smooth

If smooth = TRUE, same as qs but for the ROC curve based on the smooth log-concave density estimate.

boot.mat.smooth

If smooth = TRUE, bootstrap samples for the ROC curve based on the smoothed log-concave density estimate.

Arguments

cases

Values of the continuous variable for the cases.

controls

Values of the continuous variable for the controls.

grid

Values of 1 - specificity where confidence intervals should be computed at (may be a vector).

conf.level

Confidence level of confidence interval.

M

Number of bootstrap replicates.

smooth

Logical. Compute confidence interval also for ROC curve estimate based on smoothed log-concave densities.

output

Logical. Show progress of computations?

Author

Kaspar Rufibach (maintainer)

http://www.kasparrufibach.ch.

References

The reference for computation of these bootstrap confidence intervals is:

Rufibach, K. (2012). A smooth ROC curve estimator based on log-concave density estimates. Int. J. Biostat., 8(1), 1--29.

The bootstrap competitor based on the empirical ROC curve is described in:

Zhou, X.H. and Qin, G. (2005). Improved confidence intervals for the sensitivity at a fixed level of specificity of a continuous-scale diagnostic test. Statist. Med., 24, 465--477.

See Also

The ROC curve based on log-concave density estimates can be computed using logConROC. In the example below we analyze the pancreas data.

Examples

if (FALSE) {
## ROC curve for pancreas data 
data(pancreas)
status <- factor(pancreas[, "status"], levels = 0:1, labels = c("healthy", "diseased"))
var <- log(pancreas[, "ca199"])
cases <- var[status == "diseased"]
controls <- var[status == "healthy"]

## compute confidence intervals
res <- confIntBootLogConROC_t0(controls, cases, grid = c(0.2, 0.8), conf.level = 0.95, 
    M = 1000, smooth = TRUE, output = TRUE)
res
}