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).
USAGE:
predict(object, newdata, subset, pool, asList, se.fit)
REQUIRED ARGUMENTS:
object
an object inheriting from class
lmList, representing
a list of
lm objects with a common model.
OPTIONAL ARGUMENTS:
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.
VALUE:
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.