boot.roc {adjROC} | R Documentation |
boot.roc
Description
computes bootstrap AUC and AUCPR for the ROC curve
Usage
boot.roc(
score,
class,
metric = "AUC",
n = 100,
method = "emp",
event_level = "first"
)
Arguments
score |
A numeric array of diagnostic score i.e. the estimated probability of each diagnosis |
class |
A numeric array of equal length of |
metric |
character. specify the metric of interest which can be
|
n |
number of bootstrap samples. |
method |
Specifies the method for estimating the ROC curve. Three methods
are supported, which are |
event_level |
character. only needed for bootstrapping AUCPR. this
argument specifies which level of the "class" should be
considered the positive event. the values can only be
|
Value
list including mean and CI of bootstrap value (sensitivity, specificity, or the crossing point) and the bootstrap data.
Examples
# random classification and probability score
score <- runif(10000, min=0, max=1)
class <- sample(x = c(1,0), 10000, replace=TRUE)
# calculate bootstrap AUC of the ROC curve
boot.roc(score = score, class = class, n = 100, metric = "AUC")
# calculate bootstrap AUCPR of the ROC curve
boot.roc(score = score, class = class, n = 100, metric = "AUCPR")