Data is partitioned according to the levels of the grouping
g and individual
lm fits are obtained for each
data partition, using the model defined in
lmList(object, data, level, subset, na.action = na.fail, pool = TRUE, warn.lm = TRUE) # S3 method for lmList update(object, formula., …, evaluate = TRUE) # S3 method for lmList print(x, pool, …)
either a linear formula object of the form
y ~ x1+...+xn | g
groupedData object. In the formula object,
represents the response,
x1,...,xn the covariates, and
g the grouping factor specifying the partitioning of the data
according to which different
lm fits should be performed. The
g may be omitted from the formula, in which
case the grouping structure will be obtained from
must inherit from class
groupedData. The method function
lmList.groupedData is documented separately.
For the method
object is an object
inheriting from class
a two-sided linear formula with the common model for the individuals
Changes to the formula -- see
a data frame in which to interpret the variables named in
an optional integer specifying the level of grouping to be used when multiple nested levels of grouping are present.
an optional expression indicating which subset of the rows of
data should be used in the fit. This can be a logical
vector, or a numeric vector indicating which observation numbers are
to be included, or a character vector of the row names to be
included. All observations are included by default.
a function that indicates what should happen when the
NAs. The default action (
lmList to print an error message and terminate if there are any
an optional logical value indicating whether a pooled estimate of the residual standard error should be used in calculations of standard deviations or standard errors for summaries.
an object inheriting from class
lmList to be printed.
some methods for this generic require additional arguments. None are used in this method.
TRUE evaluate the new call else return the call.
a list of
lm objects with as many components as the number of
groups defined by the grouping factor. Generic functions such as
update have methods that can be applied to an
Pinheiro, J.C., and Bates, D.M. (2000) "Mixed-Effects Models in S and S-PLUS", Springer.