ci.thresholds(pROC) | Spotfire S+ 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(x, ...) ## S3 method for class 'roc': ci.thresholds(roc, conf.level=0.95, boot.n=2000, boot.stratified=TRUE, thresholds = "local maximas", ...) ## S3 method for class 'smooth.roc': ci.thresholds(smooth.roc, ...) ## S3 method for class 'formula': ci.thresholds(formula, data, ...) ## Default S3 method: ci.thresholds(response, predictor, ...)
x |
a roc object from the roc function (for ci.thresholds.roc), a formula (for ci.thresholds.formula) or a response vector (for ci.thresholds.default). |
roc |
a “roc” object from the roc function. |
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 roc function. |
formula, data |
a formula (and possibly a data object) of type
response~predictor for the roc function.
|
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
roc$thresholds ) or a character “all”, “local
maximas” or “best”.
|
... |
further arguments passed to or from other methods,
especially arguments for roc and ci.thresholds.roc
when calling ci.thresholds.default or ci.thresholds.formula .
|
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
.
For more details about the bootstrap, see the Bootstrap section in this package's documentation.
A list of length 2 and class “ci.thresholds”, 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. |
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, 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
.
Tom Fawcett (2006) ``An introduction to ROC analysis''. Pattern Recognition Letters 27, 861–874. DOI: 10.1016/j.patrec.2005.10.010.
James Carpenter and John Bithell (2000) ``Bootstrap condence intervals: when, which, what? A practical guide for medical statisticians''. Statistics in Medicine 19, 1141–1164.
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
data(aSAH) ## Not run: # Syntax (response, predictor): ci.thresholds(aSAH$outcome, aSAH$s100b) # With a roc object: rocobj <- roc(aSAH$outcome, aSAH$s100b) ci.thresholds(rocobj) # Customized bootstrap and specific thresholds: ci.thresholds(aSAH$outcome, aSAH$s100b, boot.n=500, conf.level=0.9, stratified=FALSE, thresholds=c(0.5, 1, 2)) ## End(Not run) # Alternatively, you can get the CI directly from roc(): rocobj <- roc(aSAH$outcome, aSAH$s100b, ci=TRUE, of="thresholds") rocobj$ci # Plotting the CI plot(rocobj) plot(rocobj$ci)