If the grouping factor corresponding to `object`

is included
in `newdata`

, the data frame is partitioned according to the
grouping factor levels; else, `newdata`

is repeated for all
`lm`

components. The predictions and, optionally, the standard
errors for the predictions, are obtained for each `lm`

component of `object`

, using the corresponding element of the
partitioned `newdata`

, and arranged into a list with as many
components as `object`

, or combined into a single vector or data
frame (if `se.fit=TRUE`

).

```
# S3 method for lmList
predict(object, newdata, subset, pool, asList, se.fit, ...)
```

a list with components given by the predictions (and, optionally, the
standard errors for the predictions) from each `lm`

component of `object`

, a vector with the predictions from all

`lm`

components of `object`

, or a data frame with columns
given by the predictions and their corresponding standard errors.

- object
an object inheriting from class

`"lmList"`

, representing a list of`lm`

objects with a common model.- newdata
an optional data frame to be used for obtaining the predictions. All variables used in the

`object`

model formula must be present in the data frame. If missing, the same data frame used to produce`object`

is used.- subset
an optional character or integer vector naming the

`lm`

components of`object`

from which the predictions are to be extracted. Default is`NULL`

, in which case all components are used.- asList
an optional logical value. If

`TRUE`

, the returned object is a list with the predictions split by groups; else the returned value is a vector. Defaults to`FALSE`

.- pool
an optional logical value indicating whether a pooled estimate of the residual standard error should be used. Default is

`attr(object, "pool")`

.- se.fit
an optional logical value indicating whether pointwise standard errors should be computed along with the predictions. Default is

`FALSE`

.- ...
some methods for this generic require additional arguments. None are used in this method.

José Pinheiro and Douglas Bates bates@stat.wisc.edu

`lmList`

, `predict.lm`

```
fm1 <- lmList(distance ~ age | Subject, Orthodont)
predict(fm1, se.fit = TRUE)
```

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