boot.adjroc {adjROC}

boot.adjroc

Description

computes bootstrap adjusted sensitivity, bootstrap adjusted specificity, or bootstrap crossing point between sensitivity and specificity for different thresholds

Usage

boot.adjroc(
  score,
  class,
  n = 100,
  method = "emp",
  sensitivity = NULL,
  specificity = NULL
)

Arguments

score

A numeric array of diagnostic score i.e. the estimated probability of each diagnosis

class

A numeric array of equal length of "score", including the actual class of the observations

n

number of bootstrap samples.

method

Specifies the method for estimating the ROC curve. Three methods are supported, which are "empirical", "binormal", and "nonparametric"

sensitivity

numeric. Specify the threshold of sensitivity.

specificity

numeric. Specify the threshold of specificity.

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 adjusted sensitivity, when specificity threshold is 0.90:
adjroc(score = score, class = class, specificity = 0.9, plot = TRUE)

# calculate adjusted specificity, when sensitivity threshold equals 0.9
boot.adjroc(score = score, class = class, n = 100, sensitivity = 0.9)

# calculate the bootstrap meeting point between sensitivity and specificity
boot.adjroc(score = score, class = class, n = 100)