Compute the confidence interval of specificities at given sensitivities

This function computes the confidence interval (CI) of the specificity at the given sensitivity points.

Usage

# ci.sp(...)
# S3 method for class 'roc'
ci.sp(roc, sensitivities = seq(0, 1, .1) * ifelse(roc$percent,
100, 1), conf.level=0.95, boot.n=2000, boot.stratified=TRUE,
progress = NULL, parallel=FALSE, ...) 
# S3 method for class 'smooth.roc'
ci.sp(smooth.roc, sensitivities = seq(0, 1, .1) *
ifelse(smooth.roc$percent, 100, 1), conf.level=0.95, boot.n=2000,
boot.stratified=TRUE, progress = NULL, 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: DEPRECATED. A value other than NULL will produce a warning.The argument will be removed in a future version.
parallel: DEPRECATED. A value other than FALSE will produce a warning. The argument will be removed in a future version.
...: 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.

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.

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.

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: 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: 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: 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: doi:10.18637/jss.v040.i01 .