e.scal: Function to calculate e- or softmax scaled membership values

In klaR: Classification and Visualization

Function to calculate e- or softmax scaled membership values

Description

Calculates the e- or softmax scaled membership values of an argmax based classification rule.

Usage

e.scal(x, k = 1, tc = NULL)

Arguments

x	matrix of membership values
k	parameter for e-scaling (1 for softmax)
tc	vector of true classes (required if k has to be optimized)

Details

For any membership vector y \exp(y\cdot k) / \sum\exp(y\cdot k) is calculated. If k=1, the classical softmax scaling is used. If the true classes are given, k is optimized so that the apparent error rate is minimized.

Value

A list containing elements

sv	Scaled values
k	Optimal k

Author(s)

Karsten Luebke, [e-mail]

References

Garczarek, Ursula Maria (2002): Classification rules in standardized partition spaces. Dissertation, University of Dortmund. URL http://hdl.handle.net/2003/2789

Examples

library(MASS)
data(iris)
ldaobj <- lda(Species ~ ., data = iris)
ldapred <- predict(ldaobj)$posterior
e.scal(ldapred)
e.scal(ldapred, tc = iris$Species)

klaR documentation built on March 31, 2023, 3:03 p.m.