Mabayes: Membership assignment by weighted Mahalanobis distance and...

In DA: Discriminant Analysis for Evolutionary Inference

Description

The function gives the discrimintion of the potential classes based on Bayes rule and the Mahalanobis distance. This function adopts the function from Bingpei Wu, 2012, WMDB 1.0 with some corrections of the judement rule.

Usage

Mabayes(TrnX, TrnG, p = rep(1, length(levels(TrnG))), TstX = NULL, var.equal = FALSE, tol)

Arguments

TrnX	Training data
TrnG	Training label
p	prior or proportion of each class
TstX	Test data
var.equal	whether the variance or the weight is equal between classes
tol	The threshold or tolerance value for the covariance and distance

Value

posterior and class

The posterior possibility and class labels

Author(s)

[e-mail]

References

Bingpei Wu, 2012, WMDB 1.0: Discriminant Analysis Methods by Weight Mahalanobis Distance and bayes.

Ito, Y., Srinivasan, C., Izumi, H. (2006, September). Discriminant analysis by a neural network with Mahalanobis distance. In International Conference on Artificial Neural Networks (pp. 350-360). Springer, Berlin, Heidelberg.

Wolfel, M., Ekenel, H. K. (2005, September). Feature weighted Mahalanobis distance: improved robustness for Gaussian classifiers. In 2005 13th European signal processing conference (pp. 1-4). IEEE.

Examples

data(iris)
train=Mabayes(iris[,1:4],iris[,5],TstX= iris[1:10,1:4],tol = 1)

DA documentation built on July 12, 2021, 9:07 a.m.