predict.merMod {lme4}  R Documentation 
The predict
method for merMod
objects, i.e. results of lmer()
, glmer()
, etc.
## S3 method for class 'merMod'
predict(object, newdata = NULL, newparams = NULL,
re.form = NULL, ReForm, REForm, REform,
random.only=FALSE, terms = NULL,
type = c("link", "response"), allow.new.levels = FALSE,
na.action = na.pass, ...)
object 
a fitted model object 
newdata 
data frame for which to evaluate predictions. 
newparams 
new parameters to use in evaluating predictions,
specified as in the 
re.form 
(formula, 
ReForm , REForm , REform 
allowed for backward compatibility: 
random.only 
(logical) ignore fixed effects, making predictions only using random effects? 
terms 
a 
type 
character string  either 
allow.new.levels 
logical if new levels (or NA values) in

na.action 

... 
optional additional parameters. None are used at present. 
If any random effects are included in re.form
(i.e. it is not ~0
or NA
),
newdata
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 NA
in this case.
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 bootMer
for this task.
a numeric vector of predicted values
(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 (level0)
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, level0
str(p4 < predict(gm1,newdata,re.form= ~(1herd))) # explicitly specify RE
stopifnot(identical(p2, p4))