ci.coords {pROC} | R Documentation |
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 |
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.
A matrix of class “ci.coords”, “ci” and “matrix” (in this order), with the confidence
intervals of the CI. The matrix has 3 columns (lower bound, median and upper bound) and
as many rows as x
* ret
were requested. Rows are sorted by x
and then by ret
and named as “input x: return”.
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. |
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: 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.
data(aSAH) ## Not run: # Syntax (response, predictor): ci.coords(aSAH$outcome, aSAH$s100b, x="best", input = "threshold", ret=c("specificity", "ppv", "tp")) # With a roc object: rocobj <- roc(aSAH$outcome, aSAH$s100b) ci.coords(rocobj, x=0.9, input = "sensitivity", ret="specificity") ci.coords(rocobj, x=0.9, input = "sensitivity", ret=c("specificity", "ppv", "tp")) ci.coords(rocobj, x=c(0.1, 0.5, 0.9), input = "sensitivity", ret="specificity") ci.coords(rocobj, x=c(0.1, 0.5, 0.9), input = "sensitivity", ret=c("specificity", "ppv", "tp")) # With a smoothed roc: rocobj <- roc(aSAH$outcome, aSAH$s100b) ci.coords(smooth(rocobj), x=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(rocobj, x="best", input = "threshold", ret=rets) ## End(Not run)