scale.glc: Scale method for the class 'glc' and 'gqc'

In matsukik/grt: General Recognition Theory

Description

Return the discriminant scores obtained by applying the general linear classifier to the fitted data.

Usage

## S3 method for class 'glc'
scale(x, initdb = FALSE, zlimit = Inf, ...)
## S3 method for class 'gqc'
scale(x, initdb = FALSE, zlimit = Inf, ...)

Arguments

x	object of class glc or gqc
initdb	optional logical. If TRUE, the returned vector represents the z-scores with respect to the initial parameters, rather than the fitted parameters. Defaults to FALSE.
zlimit	optional numeric. Used to truncate the scores beyond the speficied value. Default to Inf
...	further arguments (currently unused)

Note

The generic function scale is redefined to accept arguments other than x, center, and scale.

Examples

data(subjdemo_2d)
fit.2dl <- glc(response ~ x + y, data=subjdemo_2d, 
    category=subjdemo_2d$category, zlimit=7)
scale(fit.2dl)

fit.2dq <- gqc(response ~ x + y, data=subjdemo_2d, 
    category=subjdemo_2d$category, zlimit=7)
scale(fit.2dq)


## Not run: 
#plots using the discriminant scores
require(Hmisc)
options(digits=3)
fit.2dl <- glc(response ~ x + y, data=subjdemo_2d, 
    category=subjdemo_2d$category, zlimit=7)
# z-scores based on the initial decision bound
# split by the true category membership
zinit <- split(scale(fit.2dl, initdb=TRUE), subjdemo_2d$category)
histbackback(zinit)

# z-scores based on the fitted decision bound
# split by the participants' response
zfit1 <- split(scale(fit.2dl, initdb=FALSE), subjdemo_2d$category)
histbackback(zfit1)

# z-scores based on the fitted decision bound
# split by the true category membership
zfit2 <- split(scale(fit.2dl, initdb=FALSE), subjdemo_2d$response)
histbackback(zfit2)

## End(Not run)

matsukik/grt documentation built on May 21, 2019, 12:57 p.m.