Data
is partitioned according to the levels of the grouping
factor g
and individual lm
fits are obtained for each
data
partition, using the model defined in object
.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, …)
lmList
,
either a linear formula object of the form y ~ x1+...+xn | g
or a groupedData
object. In the formula object, y
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
grouping factor g
may be omitted from the formula, in which
case the grouping structure will be obtained from data
, which
must inherit from class groupedData
. The method function
lmList.groupedData
is documented separately.
For the method update.lmList
, object
is an object
inheriting from class lmList
.
update.lmList
only)
a two-sided linear formula with the common model for the individuals
lm
fits.
update.formula
for
details.object
.
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.NA
s. The default action (na.fail
) causes
lmList
to print an error message and terminate if there are any
incomplete observations.
lmList
to be printed.TRUE
evaluate the new call else return the call.lm
objects with as many components as the number of
groups defined by the grouping factor. Generic functions such as
coef
, fixed.effects
, lme
, pairs
,
plot
, predict
, random.effects
, summary
,
and update
have methods that can be applied to an lmList
object.lm
,
lme.lmList
,
plot.lmList
,
pooledSD
,
predict.lmList
,
residuals.lmList
,
summary.lmList
fm1 <- lmList(distance ~ age | Subject, Orthodont)
summary(fm1)
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