logcondens: confIntBootLogConROC_t0 – R documentation

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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:

`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.

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.

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)

logcondens

Estimate a Log-Concave Probability Density from Iid Observations

v2.1.5

GPL (>= 2)

Authors

Kaspar Rufibach <kaspar.rufibach@gmail.com> and Lutz Duembgen <duembgen@stat.unibe.ch>

Initial release

2016-07-11