lmList {lme4} | R Documentation |
Fit a list of lm
objects with a common model for
different subgroups of the data.
lmList(formula, data, family, subset, weights, na.action, offset, pool = TRUE, ...)
formula |
a linear |
family |
an optional family specification for a generalized linear model. |
pool |
logical scalar, should the variance estimate pool the residual sums of squares |
... |
additional, optional arguments to be passed to the model function or family evaluation. |
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 |
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).
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