grtMeans: Obtain means of two multivariate normal populations satistying certain criteria

Description

Obtain means of two multivariate normal populations having the specified covariance structure and centroid, and with which classification based on the optimal decision boundary satisfies the supplied probability of correct classification.

Usage

grtMeans(covs, centroid, optldb, p.correct, initd = 5, stepsize = 1)

Arguments

covs

a matrix or a list of matrices specifying the covariance matrices of the variables. Each matrix should be positive-definite and symmetric.

centroid

a vector specifying the center of the two population means

optldb

object of class glcStruct or a vector of coefficients for the optimal linear decision bound.

p.correct

a numeric value between 0 to 1 that specify the optimal classification performance in terms of probality of correct classification given the decision boundary optbnd.

initd

numeric. An initial distance between the means of two populations. Default is 5.

stepsize

a positive numeric specifying step size to be taken when searching for the means. Default is 1.

Value

meansa list of two vectors specifying the means of two populations.
covsa matrix of (averaged) covariance.
p.correctthe obtained probability of correct classification.

References

Alfonso-Reese, L. A. (2006) General recognition theory of categorization: A MATLAB toolbox. Behavior Research Methods, 38, 579-583.

Examples

Run this code

foo <- grtMeans(diag(c(625,625)), centroid=c(200, 200*.6), 
    optldb=c(.6,-1,0), p.correct=.85)