# 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.

- Keywords
- utilities, nonparametric, univar, ROC

##### 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.

##### 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.

##### 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:

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.

##### 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.

##### 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.

##### See Also

CRAN package plyr, employed in this function.

##### Examples

```
# 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"))
# }
```

*Documentation reproduced from package pROC, version 1.16.2, License: GPL (>= 3)*