# NOT RUN {
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)
# }
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