Compute the confidence interval of sensitivities at given...

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ci.seR Documentation

Compute the confidence interval of sensitivities at given specificities


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


## S3 method for class 'roc', specificities = 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', specificities = 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', data, ...)
## Default S3 method:, predictor, ...)


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.


on which specificities to evaluate the CI.


the width of the confidence interval as [0,1], never in percent. Default: 0.95, resulting in a 95% CI.


the number of bootstrap replicates. Default: 2000.


should the bootstrap be stratified (default, same number of cases/controls in each replicate than in the original sample) or not.


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.


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 when calling or Arguments for txtProgressBar (only char and style) if applicable.

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

The function creates boot.n bootstrap replicate of the ROC curve, and evaluates the sensitivity at specificities given by the specificities 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.


A matrix of class “”, “ci” and “matrix” (in this order) containing the given sensitivities. Row (names) are the specificities, 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:


the width of the CI, in fraction.


the number of bootstrap replicates.


whether or not the bootstrapping was stratified.


the specificities as given in argument.


the object of class “roc” that was used to compute the CI.


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.


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.


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")}.

See Also

roc, ci, ci.sp,


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

## Basic example ##
## Not run:
## End(Not run)

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

## Plotting the CI ##
ci1 <-, boot.n = 10)

## On smoothed ROC curves with bootstrap ##
## Not run:, method="density"))
## End(Not run)

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