qdb {grt} | R Documentation |
Quadratic Decision Bound
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
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)