This function computes the confidence interval (CI) of an area under the curve (AUC). By default, the 95% CI is computed with 2000 stratified bootstrap replicates.
# ci.auc(...)
# S3 method for roc
ci.auc(roc, conf.level=0.95, method=c("delong",
"bootstrap"), boot.n = 2000, boot.stratified = TRUE, reuse.auc=TRUE,
progress = getOption("pROCProgress")$name, parallel=FALSE, ...)
# S3 method for smooth.roc
ci.auc(smooth.roc, conf.level=0.95, boot.n=2000,
boot.stratified=TRUE, reuse.auc=TRUE,
progress=getOption("pROCProgress")$name, parallel=FALSE, ...)
# S3 method for auc
ci.auc(auc, ...)
# S3 method for multiclass.roc
ci.auc(multiclass.roc, ...)
# S3 method for multiclass.auc
ci.auc(multiclass.auc, ...)
# S3 method for auc
ci.auc(auc, ...)
# S3 method for formula
ci.auc(formula, data, ...)
# S3 method for default
ci.auc(response, predictor, ...)an “auc” object from the auc function.
not implemented.
arguments for the roc function.
a formula (and possibly a data object) of type
response~predictor for the roc function.
the width of the confidence interval as [0,1], never in percent. Default: 0.95, resulting in a 95% CI.
the method to use, either “delong” or “bootstrap”. The first letter is sufficient. If omitted, the appropriate method is selected as explained in details.
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.
if TRUE (default) and the “roc” object
contains an “auc” field, re-use these specifications for the
test. If false, use optional … arguments to
auc. See details.
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.
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 roc.test.roc
when calling roc.test.default or roc.test.formula.
Arguments for auc
and txtProgressBar (only char and style)
if applicable.
A numeric vector of length 3 and class “ci.auc”, “ci” and “numeric” (in this order), with the lower bound, the median and the upper bound of the CI, and the following attributes:
the width of the CI, in fraction.
the method employed.
the number of bootstrap replicates.
whether or not the bootstrapping was stratified.
an object of class “auc” stored for reference about the compued AUC details (partial, percent, ...)
The aucs item is not included in this list since version 1.2 for consistency reasons.
The comparison of the CI needs a specification of the AUC. This allows to compute the CI for full or partial AUCs. The specification is defined by:
the “auc” field in the “roc” object if
reuse.auc is set to TRUE (default). It is naturally
inherited from any call to roc and fits most cases.
passing the specification to auc with …
(arguments partial.auc, partial.auc.correct and
partial.auc.focus). In this case, you must ensure either that
the roc object do not contain an auc field (if
you called roc with auc=FALSE), or set
reuse.auc=FALSE.
If reuse.auc=FALSE the auc function will always
be called with … to determine the specification, even if
the “roc” object do contain an auc field.
As well if the “roc” object do not contain an auc
field, the auc function will always be called with
… to determine the specification.
Warning: if the roc object passed to ci contains an auc
field and reuse.auc=TRUE, auc is not called and
arguments such as partial.auc are silently ignored.
If method="delong" and the AUC specification specifies a
partial AUC, the warning “Using DeLong's test for partial AUC is
not supported. Using bootstrap test instead.” is issued. The
method argument is ignored and “bootstrap” is used
instead.
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.
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.
This function computes the CI of an AUC. Two methods are available:
“delong” and “bootstrap” with the parameters defined in “roc$auc” to
compute a CI. When it is called with two vectors (response, predictor)
or a formula (response~predictor) arguments, the roc
function is called to build the ROC curve first.
The default is to use
“delong” method except for comparison of partial AUC and smoothed
curves, where bootstrap is used. Using “delong” for
partial AUC and smoothed ROCs is not supported.
With method="bootstrap", the function calls auc
boot.n times. 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.
With method="delong", the variance of the AUC is computed as
defined by DeLong et al. (1988) using the algorithm by Sun and Xu (2014)
and the CI is deduced with qnorm.
CI of multiclass ROC curves and AUC is not implemented yet. Attempting to call these methods returns an error.
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.
Elisabeth R. DeLong, David M. DeLong and Daniel L. Clarke-Pearson (1988) ``Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach''. Biometrics 44, 837--845.
Xu Sun and Weichao Xu (2014) ``Fast Implementation of DeLongs Algorithm for Comparing the Areas Under Correlated Receiver Operating Characteristic Curves''. IEEE Signal Processing Letters, 21, 1389--1393. DOI: 10.1109/LSP.2014.2337313.
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: 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: www.jstatsoft.org/v40/i01.
# NOT RUN {
# Create a ROC curve:
data(aSAH)
roc1 <- roc(aSAH$outcome, aSAH$s100b)
## Basic example ##
ci.auc(roc1)
# You can also write:
ci(roc1)
ci(auc(roc1))
## More options ##
# Partial AUC and customized bootstrap:
# }
# NOT RUN {
ci.auc(roc1,
conf.level=0.9,
partial.auc=c(1, .8), partial.auc.focus="se", partial.auc.correct=TRUE,
boot.n=10000, stratified=FALSE)
# }
# NOT RUN {
# Note that the following will NOT give a CI of the partial AUC:
# }
# NOT RUN {
ci.auc(roc1,
partial.auc=c(1, .8), partial.auc.focus="se", partial.auc.correct=FALSE)
# }
# NOT RUN {
# This is because rocobj$auc is not a partial AUC and reuse.auc = TRUE by default.
# You can overcome this problem by passing an AUC instead:
auc1 <- auc(roc1, partial.auc=c(1, .8), partial.auc.focus="se",
partial.auc.correct=FALSE)
# }
# NOT RUN {
ci.auc(auc1)
# }
# NOT RUN {
## On smoothed ROC curves with bootstrap ##
# }
# NOT RUN {
ci.auc(smooth(roc1, method="density"))
# }
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