| ucpm {klaR} | R Documentation |
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 |
tc |
vector of true classes |
ec |
vector of estimated classes (only required if scaled membership values are used) |
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.
Value
A list with elements:
CR |
Correctness Rate |
AC |
Accuracy |
AS |
Ability to Seperate |
CF |
Confidence |
CFvec |
Confidence for each (true) class |
Author(s)
Karsten Luebke, karsten.luebke@fom.de
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)
ucpm(predict(lda(Species ~ ., data = iris))$posterior, iris$Species)