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\name{predict.clm2}
\alias{predict.clm2}
\alias{predict.clmm2}
\title{Predict Method for CLM fits}
\description{
Obtains predictions from a cumulative link (mixed) model.
}
\usage{
\method{predict}{clm2}(object, newdata, ...)
%% \method{predict}{clmm}(object, newdata, ...)
}
\arguments{
\item{object}{a fitted object of class inheriting from
\code{clm2} including \code{clmm2} objects.}
\item{newdata}{optionally, a data frame in which to look for variables
with which to predict. Observe that the response variable should
also be present.}
\item{\dots}{further arguments passed to or from other methods.}
}
\details{
This method does not duplicate the behavior of
\code{predict.polr} in package \code{MASS} which produces a
matrix instead of a vector of predictions. The behavior of
\code{predict.polr} can be mimiced as shown in the examples.
If \code{newdata} is not supplied, the fitted values are obtained. For
\code{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 \code{newdata}
argument. For \code{clm2} fits those two sets of predictions are
identical.
}
\value{
A vector of predicted probabilities.
}
\author{Rune Haubo B Christensen}
\seealso{
\code{\link[ordinal]{clm2}}, \code{\link[ordinal]{clmm2}}.
}
\examples{
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
}
}
\keyword{internal}
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