grtMeans: Obtain means of two multivariate normal populations...

In grt: General Recognition Theory

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 specifies the optimal classification performance in terms of probability 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

means	a list of two vectors specifying the means of two populations.
covs	a matrix of (averaged) covariance.
p.correct	the obtained probability of correct classification.

Author(s)

Author of the original Matlab routine ‘Design2dGRTexp’: Leola Alfonso-Reese

Author of R adaptation: Kazunaga Matsuki

References

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

See Also

ldb.p.correct

Examples

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

Example output

Loading required package: MASS

Attaching package: 'grt'

The following object is masked from 'package:base':

    scale

grt documentation built on May 2, 2019, 7:10 a.m.