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\name{predict.glmmPQL}
\alias{predict.glmmPQL}
\title{Predict Method for glmmPQL Fits}
\description{
Obtains predictions from a fitted generalized linear model
with random effects.
}
\usage{
\method{predict}{glmmPQL}(object, newdata = NULL, type = c("link", "response"),
level, na.action = na.pass, ...)
}
\arguments{
\item{object}{a fitted object of class inheriting from \code{"glmmPQL"}.}
\item{newdata}{optionally, a data frame in which to look for variables with
which to predict.}
\item{type}{the type of prediction required. The default is on the
scale of the linear predictors; the alternative \code{"response"}
is on the scale of the response variable. Thus for a default
binomial model the default predictions are of log-odds (probabilities
on logit scale) and \code{type = "response"} gives the predicted
probabilities.}
\item{level}{an optional integer vector giving the level(s) of grouping
to be used in obtaining the predictions. Level values increase from
outermost to innermost grouping, with level zero corresponding to the
population predictions. Defaults to the highest or innermost level of
grouping.}
\item{na.action}{function determining what should be done with missing
values in \code{newdata}. The default is to predict \code{NA}.}
\item{\dots}{further arguments passed to or from other methods.}
}
\value{
If \code{level} is a single integer, a vector otherwise a data frame.
}
\seealso{
\code{\link{glmmPQL}}, \code{\link[nlme]{predict.lme}}.
}
\examples{
fit <- glmmPQL(y ~ trt + I(week > 2), random = ~1 | ID,
family = binomial, data = bacteria)
predict(fit, bacteria, level = 0, type="response")
predict(fit, bacteria, level = 1, type="response")
}
\keyword{models}
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