Predictions from a model at new data values
## 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 (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))
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