pROC (version 1.16.2)

# ci.coords: Compute the confidence interval of arbitrary coordinates

## Description

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.

## Usage

```# 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,
boot.stratified=TRUE,
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,
boot.stratified=TRUE,
progress=getOption("pROCProgress")\$name, ...)
# S3 method for default
ci.coords(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.

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”.

best.policy

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.

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.

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.

## Value

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:

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.

input

the input coordinate, as given in argument.

x

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

ret

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

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, 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.

## Details

`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`.

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: 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: 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: www.jstatsoft.org/v40/i01.

`roc`, `coords`, `ci`

CRAN package plyr, employed in this function.

## Examples

Run this code
``````# NOT RUN {
# Create a ROC curve:
data(aSAH)
roc1 <- roc(aSAH\$outcome, aSAH\$s100b)

## Basic example ##
# }
# NOT RUN {
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)
# }
# NOT RUN {

## On smoothed ROC curves with bootstrap ##
# }
# NOT RUN {
ci.coords(smooth(roc1), x=0.9, input = "sensitivity", ret=c("specificity", "ppv", "tp"))
# }
``````

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