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.
# ci.coords(...)
## S3 method for class '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")$name, ...)
## S3 method for class 'formula'
ci.coords(formula, data, ...)
## S3 method for class 'smooth.roc'
ci.coords(smooth.roc, x,
input=c("specificity", "sensitivity"), ret=c("specificity", "sensitivity"),
best.method=c("youden", "closest.topleft"), best.weights=c(1, 0.5),
best.policy = c("stop", "omit", "random"),
conf.level=0.95, boot.n=2000,
boot.stratified=TRUE,
progress=getOption("pROCProgress")$name, ...)
## Default S3 method:
ci.coords(response, predictor, ...)roc, smooth.roc |
a “roc” object from the
|
response, predictor |
arguments for the |
formula, data |
a formula (and possibly a data object) of type
response~predictor for the |
x, input, ret, best.method, best.weights |
Arguments passed to |
best.policy |
The policy follow when multiple “best” thresholds are returned by |
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
|
... |
further arguments passed to or from other methods,
especially arguments for |
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.
When x="best", the best threshold is determined at each bootstrap
iteration, effectively assessing the confidence interval of choice of the "best"
threshold itself. This differs from the behavior of ci.thresholds,
where the "best" threshold is assessed on the given ROC curve before
resampling.
For more details about the bootstrap, see the Bootstrap section in this package's documentation.
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
|
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.
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.
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: 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: 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.
# 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)
## End(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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