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\name{predict.merMod}
\alias{predict.merMod}
\title{Predictions from a model at new data values}
\description{
The \code{\link{predict}} method for \code{\linkS4class{merMod}}
objects, i.e. results of \code{\link{lmer}()}, \code{\link{glmer}()}, etc.
}
\usage{
\method{predict}{merMod}(object, newdata = NULL, newparams = NULL,
re.form = NULL,
random.only=FALSE, terms = NULL,
type = c("link", "response"), allow.new.levels = FALSE,
na.action = na.pass,
se.fit = FALSE,
\dots)
}
\arguments{
\item{object}{a fitted model object}
\item{newdata}{data frame for which to evaluate
predictions.}
\item{newparams}{new parameters to use in evaluating predictions,
specified as in the \code{start} parameter for \code{\link{lmer}}
or \code{\link{glmer}} -- a list with components \code{theta} and/or
(for GLMMs) \code{beta}.}
\item{re.form}{(formula, \code{NULL}, or \code{NA}) specify which random effects to condition on when predicting. If \code{NULL},
include all random effects; if \code{NA} or \code{~0},
include no random effects.}
\item{random.only}{(logical) ignore fixed effects, making predictions
only using random effects?}
\item{terms}{a \code{\link{terms}} object - unused at present.}
\item{type}{character string - either \code{"link"}, the default, or
\code{"response"} indicating the type of prediction object returned.}
\item{allow.new.levels}{logical if new levels (or NA values) in
\code{newdata} are allowed. If FALSE (default), such new values in
\code{newdata} will trigger an error; if TRUE, then the prediction
will use the unconditional (population-level) values for data with
previously unobserved levels (or NAs).}
\item{na.action}{\code{\link{function}} determining what should be done
with missing values for fixed effects in \code{newdata}.
The default is to predict \code{NA}: see \code{\link{na.pass}}.}
\item{se.fit}{(Experimental) A logical value indicating whether the standard errors should be included or not. Default is FALSE.}
\item{...}{optional additional parameters. None are used at present.}
}
\value{
a numeric vector of predicted values
}
\details{
\itemize{
\item If any random effects are included in \code{re.form}
(i.e. it is not \code{~0} or \code{NA}),
\code{newdata} \emph{must} contain columns
corresponding to all of the grouping variables and
random effects used in the original model, even if not all
are used in prediction; however, they can be safely set to \code{NA}
in this case.
\item There is no option for computing standard errors of
predictions because it is difficult to define an
efficient method that incorporates uncertainty in the
variance parameters; we recommend \code{\link{bootMer}}
for this task.
}
}
\examples{
(gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 |herd), cbpp, binomial))
str(p0 <- predict(gm1)) # fitted values
str(p1 <- predict(gm1,re.form=NA)) # fitted values, unconditional (level-0)
newdata <- with(cbpp, expand.grid(period=unique(period), herd=unique(herd)))
str(p2 <- predict(gm1,newdata)) # new data, all RE
str(p3 <- predict(gm1,newdata,re.form=NA)) # new data, level-0
str(p4 <- predict(gm1,newdata,re.form= ~(1|herd))) # explicitly specify RE
stopifnot(identical(p2, p4))
\dontshow{
## predict() should work with variable names with spaces [as lm() does]:
dd <- expand.grid(y=1:3, "Animal ID" = 1:9)
fm <- lmer(y ~ 1 + (1 | `Animal ID`), dd)
summary(fm)
isel <- c(7, 9, 11, 13:17, 20:22)
stopifnot(all.equal(vcov(fm)[1,1], 0.02564102564),
all.equal(unname(predict(fm, newdata = dd[isel,])),
unname( fitted(fm) [isel])))
} % dontshow
} % examples
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