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, …)
```

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.

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.

```
# NOT RUN {
fm1 <- lmList(distance ~ age | Subject, Orthodont)
predict(fm1, se.fit = TRUE)
# }
```

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