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logcondens (version 2.0.6)

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)

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?

Value

  • A list containing the following elements:
  • qsdata.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.matBootstrap samples for the ROC curve based on the log-concave density estimate.
  • qs.smoothIf smooth = TRUE, same as qs but for the ROC curve based on the smooth log-concave density estimate.
  • boot.mat.smoothIf smooth = TRUE, bootstrap samples for the ROC curve based on the smoothed log-concave density estimate.

References

The reference for computation of these bootstrap confidence intervals is: Rufibach, K. (2011). A smooth ROC curve estimator based on log-concave density estimates. Preprint. 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

Run this code
## 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

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