predictOld: Predict Method for CLM fits
In ordinal: Regression Models for Ordinal Data

Description Usage Arguments Details Value Author(s) See Also Examples

Obtains predictions from a cumulative link (mixed) model.

1 2	## S3 method for class 'clm2' predict(object, newdata, ...)

`object`	a fitted object of class inheriting from `clm2` including `clmm2` objects.
`newdata`	optionally, a data frame in which to look for variables with which to predict. Observe that the response variable should also be present.
`...`	further arguments passed to or from other methods.

This method does not duplicate the behavior of predict.polr in package MASS which produces a matrix instead of a vector of predictions. The behavior of predict.polr can be mimiced as shown in the examples.

If newdata is not supplied, the fitted values are obtained. For clmm2 fits this means predictions that are controlled for the observed value of the random effects. If the predictions for a random effect of zero, i.e. an average 'subject', are wanted, the same data used to fit the model should be supplied in the newdata argument. For clm2 fits those two sets of predictions are identical.

A vector of predicted probabilities.

Rune Haubo B Christensen

clm2, clmm2.

options(contrasts = c("contr.treatment", "contr.poly"))

## More manageable data set for less voluminous printing:
(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))
dat26

m1 <- clm2(sureness ~ prod, scale = ~prod, data = dat26,
          weights = wghts, link = "logistic")
predict(m1)

mN1 <-  clm2(sureness ~ 1, nominal = ~prod, data = dat26,
            weights = wghts)
predict(mN1)

predict(update(m1, scale = ~.-prod))


#################################
## Mimicing the behavior of predict.polr:
if(require(MASS)) {
    ## Fit model from polr example:
    fm1 <- clm2(Sat ~ Infl + Type + Cont, weights = Freq, data = housing)
    predict(fm1)

    set.seed(123)
    nlev <- 3
    y <- gl(nlev, 5)
    x <- as.numeric(y) + rnorm(15)
    fm.clm <- clm2(y ~ x)
    fm.polr <- polr(y ~ x)

    ## The equivalent of predict.polr(object, type = "probs"):
    (pmat.polr <- predict(fm.polr, type = "probs"))
    ndat <- expand.grid(y = gl(nlev,1), x = x)
    (pmat.clm <- matrix(predict(fm.clm, newdata = ndat), ncol=nlev,
                        byrow = TRUE))
    all.equal(c(pmat.clm), c(pmat.polr), tol = 1e-5) # TRUE

    ## The equivalent of predict.polr(object, type = "class"):
    (class.polr <- predict(fm.polr))
    (class.clm <- factor(apply(pmat.clm, 1, which.max)))
    all.equal(class.clm, class.polr) ## TRUE
}