DET (version 3.0.0)

detc: DET Curve calculation

Description

From a response and predictors, the function calculates the DET curve for each pair (response, predictor). Optionally, it can compute this curve with a Confidence Interval (CI). Instead of a response and predictors, it can also receive a 'DETs' object to extract the results of the DET curves and compute the CIs.

Usage

detc(
  response = NULL,
  predictors = NULL,
  dets = NULL,
  names = NULL,
  conf = NULL,
  positive = "",
  parallel = FALSE,
  ncores = detectCores(),
  nboot = NULL,
  plot = FALSE,
  ...
)

Arguments

response

A factor, typically encoded with 0 (non-target) and 1 (target). Also it can be a dicothomous variable which will be treated as a factor. By default, the level of response is taken as target.

predictors

A matrix which columns represent the values of each predictor.

dets

A 'DETs' object which will be used to compute the DET curves.

names

A character vector that will be used to set the names of the DET Curves. It will also appear on the graphic legend when is plotted.

conf

If present, it represents the confidence level of the CI of the DET Curve, within [0,1]. Default: The CI will not be computed.

positive

A string with the name of the 'positive' level which is setting as reference level of 'response'.

parallel

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: 2000.

plot

If TRUE, the DETs curves will be plotted. Default: FALSE.

...

Further attributes that will be passed to the plot function.

Value

A 'DETs' object. This object contains in the attribute 'detCurves' the list of DET curves, one per classifier. Each DET curve is an object of class "DET". This object contains the parameters of the DET curve (false positive ratio, false negative ratio, and the thresholds used), along with the Equal Error Rate (EER). If the CI was calculated, it includes the median, upper and lower extremes of the CI of the false negative ratio and EER. Check the 'show' function to know more details on the outcomes saved in a 'DETs' object.

Examples

# NOT RUN {
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(
  response,
  predictors,
  positive = "target",
  names = colnames(predictors),
  conf = 0.95,
  parallel = TRUE
)
# }