View source: R/ci.thresholds.R
ci.thresholds  R Documentation 
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.
# 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, ...)
## Default S3 method:
ci.thresholds(response, predictor, ...)
roc 
a “roc” object from the 
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 
formula, data 
a formula (and possibly a data object) of type
response~predictor for the 
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

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

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 
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
.
A threshold given as a logical
vector or character
is converted to the corresponding numeric vector once
using the supplied ROC curve, and not at each bootstrap iteration. See ci.coords
for the latter behaviour.
For more details about the bootstrap, see the Bootstrap section in this package's documentation.
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. 
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
.
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: \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/(SICI)10970258(20000515)19:9<1141::AIDSIM479>3.0.CO;2F")}.
Tom Fawcett (2006) “An introduction to ROC analysis”. Pattern Recognition Letters 27, 861–874. DOI: \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.patrec.2005.10.010")}.
Xavier Robin, Natacha Turck, Alexandre Hainard, et al. (2011) “pROC: an opensource package for R and S+ to analyze and compare ROC curves”. BMC Bioinformatics, 7, 77. DOI: \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1186/147121051277")}.
Hadley Wickham (2011) “The SplitApplyCombine Strategy for Data Analysis”. Journal of Statistical Software, 40, 1–29. URL: \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v040.i01")}.
roc
,
ci
data(aSAH)
# Create a ROC curve:
data(aSAH)
roc1 < roc(aSAH$outcome, aSAH$s100b)
## Basic example ##
# Compute CI of all local maxima thresholds
## Not run:
ci.thresholds(roc1)
## End(Not run)
## More options ##
# Customized bootstrap and thresholds:
## Not run:
ci.thresholds(roc1,
thresholds=c(0.5, 1, 2),
boot.n=10000, conf.level=0.9, stratified=FALSE)
## End(Not run)
## Plotting the CI ##
## Not run:
ci1 < ci.thresholds(roc1)
## End(Not run)
plot(roc1)
plot(ci1)
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