tests/testAnova.clm2.R

In ordinal: Regression Models for Ordinal Data

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)
}