confIntBootLogConROC_t0 {logcondens} | R Documentation |
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 |
|
output |
|
Value
A list containing the following elements:
qs |
|
boot.mat |
Bootstrap samples for the ROC curve based on the log-concave density estimate. |
qs.smooth |
If |
boot.mat.smooth |
If |
Note
The confidence intervals are only valid if observations are independent, i.e. eacht patient only contributes one measurement, e.g.
Author(s)
Kaspar Rufibach (maintainer)
kaspar.rufibach@gmail.com
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
## Not run:
## 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
## End(Not run)