ci.sp: Compute the confidence interval of specificities at given... In pROC: Display and Analyze ROC Curves

 ci.sp R Documentation

Compute the confidence interval of specificities at given sensitivities

Description

This function computes the confidence interval (CI) of the specificity at the given sensitivity points. By default, the 95% CI are computed with 2000 stratified bootstrap replicates.

Usage

``````# ci.sp(...)
## S3 method for class 'roc'
ci.sp(roc, sensitivities = seq(0, 1, .1) * ifelse(roc\$percent,
100, 1), conf.level=0.95, boot.n=2000, boot.stratified=TRUE,
progress=getOption("pROCProgress")\$name, parallel=FALSE, ...)
## S3 method for class 'smooth.roc'
ci.sp(smooth.roc, sensitivities = seq(0, 1, .1) *
ifelse(smooth.roc\$percent, 100, 1), conf.level=0.95, boot.n=2000,
boot.stratified=TRUE, progress=getOption("pROCProgress")\$name, parallel=FALSE, ...)
## S3 method for class 'formula'
ci.sp(formula, data, ...)
## Default S3 method:
ci.sp(response, predictor, ...)
``````

Arguments

 `roc, smooth.roc` a “roc” object from the `roc` function, or a “smooth.roc” object from the `smooth` function. `response, predictor` arguments for the `roc` function. `formula, data` a formula (and possibly a data object) of type response~predictor for the `roc` function. `sensitivities` on which sensitivities to evaluate the CI. `conf.level` the width of the confidence interval as [0,1], never in percent. Default: 0.95, resulting in a 95% CI. `boot.n` the number of bootstrap replicates. Default: 2000. `boot.stratified` should the bootstrap be stratified (default, same number of cases/controls in each replicate than in the original sample) or not. `progress` the name of progress bar to display. Typically “none”, “win”, “tk” or “text” (see the `name` argument to `create_progress_bar` for more information), but a list as returned by `create_progress_bar` is also accepted. See also the “Progress bars” section of this package's documentation. `parallel` if TRUE, the bootstrap is processed in parallel, using parallel backend provided by plyr (foreach). `...` further arguments passed to or from other methods, especially arguments for `roc` and `ci.sp.roc` when calling `ci.sp.default` or `ci.sp.formula`. Arguments for `txtProgressBar` (only `char` and `style`) if applicable.

Details

`ci.sp.formula` and `ci.sp.default` are convenience methods that build the ROC curve (with the `roc` function) before calling `ci.sp.roc`. You can pass them arguments for both `roc` and `ci.sp.roc`. Simply use `ci.sp` that will dispatch to the correct method.

The `ci.sp.roc` function creates `boot.n` bootstrap replicate of the ROC curve, and evaluates the specificity at sensitivities given by the `sensitivities` argument. Then it computes the confidence interval as the percentiles given by `conf.level`.

For more details about the bootstrap, see the Bootstrap section in this package's documentation.

For smoothed ROC curves, smoothing is performed again at each bootstrap replicate with the parameters originally provided. If a density smoothing was performed with user-provided `density.cases` or `density.controls` the bootstrap cannot be performed and an error is issued.

Value

A matrix of class “ci.sp”, “ci” and “matrix” (in this order) containing the given specificities. Row (names) are the sensitivities, the first column the lower bound, the 2nd column the median and the 3rd column the upper bound.

Additionally, the list has the following attributes:

 `conf.level` the width of the CI, in fraction. `boot.n` the number of bootstrap replicates. `boot.stratified` whether or not the bootstrapping was stratified. `sensitivities` the sensitivities as given in argument. `roc` the object of class “roc” that was used to compute the CI.

Warnings

If `boot.stratified=FALSE` and the sample has a large imbalance between cases and controls, it could happen that one or more of the replicates contains no case or control observation, or that there are not enough points for smoothing, producing a `NA` area. The warning “NA value(s) produced during bootstrap were ignored.” will be issued and the observation will be ignored. If you have a large imbalance in your sample, it could be safer to keep `boot.stratified=TRUE`.

Errors

If `density.cases` and `density.controls` were provided for smoothing, the error “Cannot compute the statistic on ROC curves smoothed with density.controls and density.cases.” is issued.

References

James Carpenter and John Bithell (2000) “Bootstrap condence intervals: when, which, what? A practical guide for medical statisticians”. Statistics in Medicine 19, 1141–1164. DOI: \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/(SICI)1097-0258(20000515)19:9<1141::AID-SIM479>3.0.CO;2-F")}.

Tom Fawcett (2006) “An introduction to ROC analysis”. Pattern Recognition Letters 27, 861–874. DOI: \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.patrec.2005.10.010")}.

Xavier Robin, Natacha Turck, Alexandre Hainard, et al. (2011) “pROC: an open-source package for R and S+ to analyze and compare ROC curves”. BMC Bioinformatics, 7, 77. DOI: \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1186/1471-2105-12-77")}.

Hadley Wickham (2011) “The Split-Apply-Combine Strategy for Data Analysis”. Journal of Statistical Software, 40, 1–29. URL: \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v040.i01")}.

`roc`, `ci`, `ci.se`, `plot.ci`

Examples

``````# Create a ROC curve:
data(aSAH)
roc1 <- roc(aSAH\$outcome, aSAH\$s100b)

## Basic example ##
## Not run:
ci.sp(roc1)
## End(Not run)

## More options ##
# Customized bootstrap and sensitivities:
## Not run:
ci.sp(roc1, c(.95, .9, .85), boot.n=10000, conf.level=0.9, stratified=FALSE)
## End(Not run)

## Plotting the CI ##
ci1 <- ci.sp(roc1, boot.n = 10)
plot(roc1)
plot(ci1)

## On smoothed ROC curves with bootstrap ##
## Not run:
ci.sp(smooth(roc1, method="density"))
## End(Not run)
``````

pROC documentation built on Nov. 2, 2023, 6:05 p.m.