qdb: Quadratic Decision Bound

In grt: General Recognition Theory

Description

Find coefficients of the ideal quadratic decision boundary given the means and covariance of two categories.

Usage

qdb(means, covs, pnoise = 10, cnoise = 100, sphere = FALSE)

Arguments

means	a list of vectors containing means of the two distributions.
covs	a list containing the covariance matrices of the two distributions.
pnoise, cnoise	numeric. Defaults set to 10, and 100, respectively. see ‘Details’
sphere	logical. If TRUE, the returned decison bound forms a circle or sphere.

Details

The order of vectors in the list means and covs matters as the sign of coeffs and bias object in the output will be reversed.

The argument pnoise and cnoise is only for convenience; the supplied value is simply bypassed to the output for the subsequent use, i.e., as object of class gqcStruct.

Value

object of class gqcStruct

Author(s)

Author of the original Matlab routine ‘quaddecisbnd’: 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

mcovs, qdb, gqcStruct, gqc

Examples

m <- list(c(187, 142), c(213.4, 97.7))
covs <- list(diag(c(625, 625)), diag(c(625, 625)))
foo <- qdb(means=m, covs=covs)

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