pROC (version 1.18.5)

ci.coords: Compute the confidence interval of arbitrary coordinates


This function computes the confidence interval (CI) of the coordinates of a ROC curves with the coords function. By default, the 95% CI are computed with 2000 stratified bootstrap replicates.


# ci.coords(...)
# S3 method for roc
ci.coords(roc, x,
input=c("threshold", "specificity", "sensitivity"),
ret=c("threshold", "specificity", "sensitivity"),
best.method=c("youden", "closest.topleft"), best.weights=c(1, 0.5),
best.policy = c("stop", "omit", "random"),
conf.level=0.95, boot.n=2000,
progress=getOption("pROCProgress")$name, ...) 
# S3 method for formula
ci.coords(formula, data, ...)
# S3 method for smooth.roc
ci.coords(smooth.roc, x,
input=c("specificity", "sensitivity"), ret=c("specificity", "sensitivity"),
best.method=c("youden", "closest.topleft"), best.weights=c(1, 0.5),
best.policy = c("stop", "omit", "random"),
conf.level=0.95, boot.n=2000,
progress=getOption("pROCProgress")$name, ...)
# S3 method for default
ci.coords(response, predictor, ...)


Note: changed in version 1.16.

A list of the same length as ret and named as ret, and of class “ci.thresholds”, “ci” and “list” (in this order).

Each element of the list is a matrix of the confidence intervals with rows given by x and with 3 columns, the lower bound of the CI, the median, and the upper bound of the CI.

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 input coordinate, as given in argument.


the coordinates used to calculate the CI, as given in argument.


the return values, as given in argument or substituted by link{coords}.


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


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.

x, input, ret, best.method, best.weights

Arguments passed to coords. See there for more details. The only difference is on the x argument which cannot be “all” or “local maximas”.


The policy follow when multiple “best” thresholds are returned by coords. “stop” will abort the processing with stop (default), “omit” will ignore the sample (as in NA) and “random” will select one of the threshold randomly.


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.


further arguments passed to or from other methods, especially arguments for roc and ci.coords.roc when calling ci.coords.default or ci.coords.formula. Arguments for txtProgressBar (only char and style) if applicable.


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, 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.

This warning will also be displayed if you chose best.policy = "omit" and a ROC curve with multiple “best” threshold was generated during at least one of the replicates.


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

This function creates boot.n bootstrap replicate of the ROC curve, and evaluates the coordinates specified by the x, input, ret, best.method and best.weights arguments. Then it computes the confidence interval as the percentiles given by conf.level.

When x="best", the best threshold is determined at each bootstrap iteration, effectively assessing the confidence interval of choice of the "best" threshold itself. This differs from the behavior of ci.thresholds, where the "best" threshold is assessed on the given ROC curve before resampling.

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


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: 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: tools:::Rd_expr_doi("10.1016/j.patrec.2005.10.010").

Hadley Wickham (2011) ``The Split-Apply-Combine Strategy for Data Analysis''. Journal of Statistical Software, 40, 1--29. URL: tools:::Rd_expr_doi("10.18637/jss.v040.i01").

See Also

roc, coords, ci

CRAN package plyr, employed in this function.


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

## Basic example ##
if (FALSE) {
ci.coords(roc1, x="best", input = "threshold", 
          ret=c("specificity", "ppv", "tp"))

## More options ##
ci.coords(roc1, x=0.9, input = "sensitivity", ret="specificity")
ci.coords(roc1, x=0.9, input = "sensitivity", ret=c("specificity", "ppv", "tp"))
ci.coords(roc1, x=c(0.1, 0.5, 0.9), input = "sensitivity", ret="specificity")
ci.coords(roc1, x=c(0.1, 0.5, 0.9), input = "sensitivity", ret=c("specificity", "ppv", "tp"))

# Return everything we can:
rets <- c("threshold", "specificity", "sensitivity", "accuracy", "tn", "tp", "fn", "fp", "npv", 
          "ppv", "1-specificity", "1-sensitivity", "1-accuracy", "1-npv", "1-ppv")
ci.coords(roc1, x="best", input = "threshold", ret=rets)}# \dontshow{
ci.coords(roc1, x=0.9, input = "sensitivity", ret="specificity", boot.n=10)
ci.coords(roc1, x=0.9, input = "sensitivity", ret=c("specificity", "ppv", "tp"), boot.n=10)
ci.coords(roc1, x=c(0.1, 0.5, 0.9), input = "sensitivity", ret="specificity", boot.n=10)
ci.coords(roc1, x=c(0.1, 0.5, 0.9), input = "sensitivity", ret=c("specificity", "ppv", "tp"), boot.n=10)
rets <- c("threshold", "specificity", "sensitivity", "accuracy", "tn", "tp", "fn", "fp", "npv", 
          "ppv", "1-specificity", "1-sensitivity", "1-accuracy", "1-npv", "1-ppv")
ci.coords(roc1, x="best", input = "threshold", ret=rets, boot.n=10)# }

## On smoothed ROC curves with bootstrap ##
if (FALSE) {
ci.coords(smooth(roc1), x=0.9, input = "sensitivity", ret=c("specificity", "ppv", "tp"))}# \dontshow{
ci.coords(smooth(roc1), x=0.9, input = "sensitivity", ret=c("specificity", "ppv", "tp"), boot.n = 10)# }

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