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library(ordinal)
options(contrasts = c("contr.treatment", "contr.poly"))
## More manageable data set:
(tab26 <- with(soup, table("Product" = PROD, "Response" = SURENESS)))
dimnames(tab26)[[2]] <- c("Sure", "Not Sure", "Guess", "Guess", "Not Sure", "Sure")
dat26 <- expand.grid(sureness = as.factor(1:6), prod = c("Ref", "Test"))
dat26$wghts <- c(t(tab26))
m1 <- clm(sureness ~ prod, scale = ~prod, data = dat26,
weights = wghts, link = "logit")
## anova
m2 <- update(m1, scale = ~1)
anova(m1, m2)
mN1 <- clm(sureness ~ 1, nominal = ~prod, data = dat26,
link = "logit")
anova(m1, mN1)
anova(m1, m2, mN1)
## dropterm
if(require(MASS)) {
dropterm(m1, test = "Chi")
mB1 <- clm(SURENESS ~ PROD + GENDER + SOUPTYPE,
scale = ~ COLD, data = soup, link = "probit")
dropterm(mB1, test = "Chi") # or
## addterm
addterm(mB1, scope = ~.^2, test = "Chi")
## addterm(mB1, scope = ~ . + AGEGROUP + SOUPFREQ,
## test = "Chi", which = "location")
## addterm(mB1, scope = ~ . + GENDER + SOUPTYPE,
## test = "Chi", which = "scale")
## Fit model from polr example:
## data(housing, package = "MASS")
fm1 <- clm(Sat ~ Infl + Type + Cont, weights = Freq, data = housing)
## addterm(fm1, ~ Infl + Type + Cont, test= "Chisq", which = "scale")
dropterm(fm1, test = "Chisq")
fm2 <- update(fm1, scale =~ Cont)
fm3 <- update(fm1, formula =~.-Cont, nominal =~ Cont)
anova(fm1, fm2, fm3)
}
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