detc.ci: DET Curve calculation with CI

In DET: Representation of DET Curve with Confidence Intervals

Description

From a 'DETs' object, the function extracts either computes the confidence interval (CI) of each DET curve of the object.

Usage

detc.ci(
  dets = NULL,
  conf = 0.95,
  positive = "",
  parallel = TRUE,
  ncores = detectCores(),
  nboot = 2000,
  plot = FALSE,
  ...
)

Arguments

dets	A 'DETs' object which will be used to extract or compute the CIs of the DET curves.
conf	A single numeric value into the (0,1) interval, which represents the confidence level of the CI of the DET Curve. Default: conf = 0.95.
positive	A string with the name of the 'positive' level which is setting as reference level of 'response'.
parallel	Boolean. By default parallel = TRUE. If TRUE, the bootstrap method to calculated the CI is processed in parallel, using the backend provided by plyr (foreach).
ncores	The number of nodes to be forked for the parallel computation of the CI. Default: the maximum available. None used if parallel = FALSE.
nboot	The number of bootstrap replicates to be used for the computation of the CI. Default: nboot = 2000.
plot	If TRUE, the CIs will be plotted for the DET curves. Default: plot = FALSE.
...	Further attributes that will be passed to the plot function.

Value

A 'DETs' object containing the list of DET curves with their CIs, one per classifier.

Examples

library(DET)
n = 500
#Predictors with normal distribution
set.seed(1235)
scoreNegative1 = rnorm(n, mean = 0.25, sd = 0.125)
set.seed(5321)
scoreNegative2 = rnorm(n, mean = 0.25, sd = 0.125)
set.seed(11452)
scorePositive1 = rnorm(n, mean = 0.55, sd = 0.125)
set.seed(54321)
scorePositive2 = rnorm(n, mean = 0.65, sd = 0.125)
response = as.factor(c(rep(c("target"), times = n), rep(c("nontarget"), times = n)))
predictor1 = c(scoreNegative1, scorePositive1)
predictor2 = c(scoreNegative2, scorePositive2)
predictors = matrix(c(predictor1, predictor2), ncol = 2)
colnames(predictors) = c("DET1", "DET2")
detCurves = detc(
  response,
  predictors,
  positive = "target",
  names = colnames(predictors)
)

#Run in parallel for a faster execution activating logical argument 'parallel'
#and setting the number of cores of your computer
#logical argument 'parallel'
detCurvesWithConfidenceInterval = detc.ci(
  dets = detCurves,
  positive = "target",
  names = colnames(predictors),
  conf = 0.95,
  parallel = TRUE,
  ncores = 2
)

DET documentation built on April 3, 2021, 1:06 a.m.