# predict.merMod

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

The `predict`

method for `'>merMod`

objects, i.e. results of `lmer()`

, `glmer()`

, etc.

##### Usage

```
# S3 method for 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, …)
```

##### Arguments

- 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

`start`

parameter for`lmer`

or`glmer`

-- a list with components`theta`

and/or (for GLMMs)`beta`

.- re.form
formula for random effects to condition on. If

`NULL`

, include all random effects; if`NA`

or`~0`

, include no random effects.- ReForm, REForm, REform
allowed for backward compatibility:

`re.form`

is now the preferred argument name.- random.only
(logical) ignore fixed effects, making predictions only using random effects?

- terms
a

`terms`

object - unused at present.- type
character string - either

`"link"`

, the default, or`"response"`

indicating the type of prediction object returned.- allow.new.levels
logical if new levels (or NA values) in

`newdata`

are allowed. If FALSE (default), such new values in`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).- na.action
`function`

determining what should be done with missing values for fixed effects in`newdata`

. The default is to predict`NA`

: see`na.pass`

.- ...
optional additional parameters. None are used at present.

##### Details

If any random effects are included in

`re.form`

(see below),`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.

##### Value

a numeric vector of predicted values

##### Examples

```
# NOT RUN {
(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))
# }
# NOT RUN {
<!-- %dont -->
# }
```

*Documentation reproduced from package lme4, version 1.1-21, License: GPL (>= 2)*