lmList {lme4}  R Documentation 
Fit a list of lm
or glm
objects with a
common model for different subgroups of the data.
lmList(formula, data, family, subset, weights, na.action,
offset, pool = !isGLM  .hasScale(family2char(family)),
warn = TRUE, ...)
formula 
a linear 
family 
an optional 
data 
an optional data frame containing the
variables named in 
subset 
an optional expression indicating the
subset of the rows of 
weights 
an optional vector of ‘prior
weights’ to be used in the fitting process. Should be

na.action 
a function that indicates what should
happen when the data contain 
offset 
this can be used to specify an a
priori known component to be included in the linear
predictor during fitting. This should be 
pool 
logical scalar indicating if the variance estimate should
pool the residual sums of squares. By default true if the model has
a scale parameter (which includes all linear, 
warn 
indicating if errors in the single fits should signal a
“summary” 
... 
additional, optional arguments to be passed to the model function or family evaluation. 
While data
is optional, the package authors
strongly recommend its use, especially when later applying
methods such as update
and drop1
to the fitted model
(such methods are not guaranteed to work properly if
data
is omitted). If data
is omitted, variables will
be taken from the environment of formula
(if specified as a
formula) or from the parent frame (if specified as a character vector).
Since lme4 version 1.116, if there are errors (see
stop
) in the single (lm()
or glm()
)
fits, they are summarized to a warning message which is returned as
attribute "warnMessage"
and signalled as warning()
when the warn
argument is true.
In previous lme4 versions, a general (different) warning had been signalled in this case.
an object of class
lmList4
(see
there, notably for the methods
defined).
fm.plm < lmList(Reaction ~ Days  Subject, sleepstudy)
coef(fm.plm)
fm.2 < update(fm.plm, pool = FALSE)
## coefficients are the same, "pooled or unpooled":
stopifnot( all.equal(coef(fm.2), coef(fm.plm)) )
(ci < confint(fm.plm)) # print and rather *see* :
plot(ci) # how widely they vary for the individuals