# ucpm: Uschi's classification performance measures

## Description

Function to calculate the Correctness Rate, the Accuracy, the Ability to Seperate and the Confidence of
a classification rule.

## Usage

ucpm(m, tc, ec = NULL)

## Arguments

m

matrix of (scaled) membership values

ec

vector of estimated classes (only required if scaled membership values are used)

## Value

A list with elements:

CRCorrectness Rate

ACAccuracy

ASAbility to Seperate

CFConfidence

CFvecConfidence for each (true) class

## Details

The *correctness rate* is the estimator for the correctness of a classification rule (1-error rate).

The *accuracy* is based on the euclidean distances between (scaled) membership vectors and the vectors
representing the true class corner. These distances are standardized so that a measure of 1 is achieved
if all vectors lie in the correct corners and 0 if they all lie in the center.

Analougously, the *ability to seperate* is based on the distances between (scaled) membership
vectors and the vector representing the corresponding assigned class corner.

The *confidence* is the mean of the membership values of the assigned classes.

## References

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

## Examples

# NOT RUN {
library(MASS)
data(iris)
ucpm(predict(lda(Species ~ ., data = iris))$posterior, iris$Species)
# }