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,
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 for the roc function.
a formula (and possibly a data object) of type
response~predictor for the roc function.
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 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.
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.
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.
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: 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.
CRAN package plyr, employed in this function.
# 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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