# confIntBootLogConROC_t0: Function to compute a bootstrap confidence interval for the... In logcondens: Estimate a Log-Concave Probability Density from Iid Observations

## 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

 ```1 2``` ```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.

## 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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14``` ```## 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 documentation built on May 2, 2019, 6:11 a.m.