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