etc: Expected Loss of the Threshold Classifier.

In SchroederFabian/CVOC: Classification under varying operating conditions

Description

The function offers a method to select variables by univariate filtering based on the estimated loss of the optimal univariate threshold classifer. No parametric assumption about the class conditional distributions is required.

Usage

etc(class, data, oc, positive = levels(class)[1], p.val = TRUE,
  plot = FALSE, adj.method = "BH")

Arguments

class	a factor vector indicating the class membership of the instances. Must have exactly two levels.
data	a data frame with variables in columns.
oc	a vector containing three elements. oc[1], the cost of misclassifying a negative instance, oc[2], the cost of missclassifying a positive instance, and oc[3], the share of negative instances in the population.
positive	a character object indicating the factor label of the positive class.
p.val	a logical indicating whether the p-values of etc values under the null hypothesis that both classes are equal should be calculated. The exact null distribution is calculated by means of a recursive algorithm.
plot	a logical. If TRUE a plot of the null distribution will be generated.
adj.method	a character string indicating the method with which to correct the p-values for multiple testing. See ?p.adjust.

Value

a list containing three components:

etc	a numerical vector containing the etc values for every variable of dat.
p.val	the corresponding p-values of etc. (optional)
p.val.adj	the corresponding adjusted p-values of etc. (optional)

Examples

oc <- c(1,3,0.5)
class <- factor(c(rep(0, 25), rep(1, 25)), labels=c("neg", "pos"))
data <- data.frame("var1"=c(rnorm(25, 0, 1/2), rnorm(25, 1, 2)))
res <- etc(class, data, oc, positive="pos", p.val=TRUE)

SchroederFabian/CVOC documentation built on May 9, 2019, 1:18 p.m.