pROC (version 1.7.2)

ci.thresholds: Compute the confidence interval of thresholds

Description

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

Usage

# ci.thresholds(...)
## S3 method for class 'roc':
ci.thresholds(roc, conf.level=0.95, boot.n=2000,
boot.stratified=TRUE, thresholds = "local maximas",
progress=getOption("pROCProgress")$name, parallel=FALSE, ...) 
## S3 method for class 'formula':
ci.thresholds(formula, data, ...)
## S3 method for class 'smooth.roc':
ci.thresholds(smooth.roc, ...)
## S3 method for class 'default':
ci.thresholds(response, predictor, ...)

Arguments

roc

a roc object from the roc function.

smooth.roc

not available for smoothed ROC curves, available only to catch the error and provide a clear error message.

response, predictor

arguments for the roc function.

formula, data

a formula (and possibly a data object) of type response~predictor for the roc function.

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.

thresholds

on which thresholds to evaluate the CI. Either the numeric values of the thresholds, a logical vector (as index of roc$thresholds) or a character all, local maximas or best.

progress

the name of progress bar to display. Typically none, win, tk or text (see the name argument to create_progress_b

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.thresholds.roc when calling ci.thresholds.default or ci.thresholds.formula.

Value

• A list of length 2 and class ci.thresholds, ci and list (in this order), with the confidence intervals of the CI and the following items:
• specificity
  a matrix of CI for the specificity. Row (names) are the thresholds, the first column the lower bound, the 2nd column the median and the 3rd column the upper bound.
• sensitivity
  same than specificity.
• 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.
• thresholds
  the thresholds, as given in argument.
• roc
  the object of class roc that was used to compute the CI.

encoding

UTF-8

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, 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.

Details

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

This function creates boot.n bootstrap replicate of the ROC curve, and evaluates the sensitivity and specificity at thresholds given by the thresholds 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.

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: http://dx.doi.org/10.1002/(SICI)1097-0258(20000515)19:9<1141::aid-sim479>3.0.CO;2-F{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: http://dx.doi.org/10.1016/j.patrec.2005.10.010{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: http://dx.doi.org/10.1186/1471-2105-12-77{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: http://www.jstatsoft.org/v40/i01{www.jstatsoft.org/v40/i01}.

See Also

roc, ci CRAN package plyr, employed in this function.

Examples

data(aSAH)

# Syntax (response, predictor):
ci.thresholds(aSAH$outcome, aSAH$s100b)

# With a roc object:
rocobj <- roc(aSAH$outcome, aSAH$s100b)
ci.thresholds(rocobj)

# Customized bootstrap and specific thresholds:
ci.thresholds(aSAH$outcome, aSAH$s100b,
              boot.n=500, conf.level=0.9, stratified=FALSE,
              thresholds=c(0.5, 1, 2))

# Alternatively, you can get the CI directly from roc():
rocobj <- roc(aSAH$outcome,
              aSAH$s100b, ci=TRUE, of="thresholds")
rocobj$ci

# Plotting the CI
plot(rocobj)
plot(rocobj$ci)