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.
an object inheriting from class "lmList", representing
a list of lm objects with a common model.
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.
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.
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.
an optional logical value indicating whether a pooled
estimate of the residual standard error should be used. Default is
attr(object, "pool").
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)
Run the code above in your browser using DataLab