| ci(pROC) | Spotfire S+ Documentation |
This function computes the confidence interval (CI) of a ROC curve. The
of argument controls the type of CI that will be computed.
By default, the 95% CI are computed with 2000 stratified bootstrap
replicates.
ci(x, ...)
## S3 method for class 'roc':
ci(roc, of = c("auc", "thresholds", "sp", "se"), ...)
## S3 method for class 'smooth.roc':
ci(smooth.roc, of = c("auc", "sp", "se"), ...)
## S3 method for class 'formula':
ci(formula, data, ...)
## Default S3 method:
ci(response, predictor, ...)
x |
a roc object from the roc function (for ci.roc), a formula (for ci.formula) or a response vector (for ci.default). |
roc, smooth.roc |
a “roc” object from the
roc function, or a “smooth.roc” object from the
smooth.roc function.
|
response, predictor |
arguments for the roc function. |
formula, data |
a formula (and possibly a data object) of type
response~predictor for the roc function.
|
of |
The type of confidence interval. One of “auc”, “thresholds”, “sp” or “se”. Note that confidence interval on “thresholds” are not available for smoothed ROC curves. |
... |
further arguments passed to or from other methods,
especially auc, roc, and the specific
ci functions ci.auc, ci.se,
ci.sp and ci.thresholds.
|
ci.formula and ci.default are convenience methods
that build the ROC curve (with the roc function) before
calling ci.roc. You can pass them arguments for both
roc and ci.roc. Simply use ci
that will dispatch to the correct method.
This function is typically called from roc when ci=TRUE (not by
default). Depending on the of argument, the specific
ci functions ci.auc, ci.thresholds,
ci.sp or ci.se are called.
The return value of the specific ci functions
ci.auc, ci.thresholds, ci.sp
or ci.se, depending on the
of argument.
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.
roc, auc, ci.auc,
ci.thresholds, ci.sp, ci.se
data(aSAH) # Syntax (response, predictor): ci(aSAH$outcome, aSAH$s100b) # With a roc object: rocobj <- roc(aSAH$outcome, aSAH$s100b) # Of an AUC ci(rocobj) ci(rocobj, of="auc") # this is strictly equivalent to: ci.auc(rocobj) # Of thresholds, sp, se... ## Not run: ci(rocobj, of="thresholds") ci(rocobj, of="thresholds", thresholds=0.51) ci(rocobj, of="thresholds", thresholds="all") ci(rocobj, of="sp", sensitivities=c(.95, .9, .85)) ci(rocobj, of="se") ## End(Not run) # Alternatively, you can get the CI directly from roc(): rocobj <- roc(aSAH$outcome, aSAH$s100b, ci=TRUE, of="auc") rocobj$ci