predict.merMod | 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,
random.only=FALSE, terms = NULL,
type = c("link", "response"), allow.new.levels = FALSE,
na.action = na.pass,
se.fit = FALSE,
...)
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, |
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 |
|
se.fit |
(Experimental) A logical value indicating whether the standard errors should be included or not. Default is FALSE. |
... |
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))
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.