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, ...)
```

an object of `class`

`lmList4`

(see
there, notably for the `methods`

defined).

- formula
a linear

`formula`

object of the form`y ~ x1+...+xn | g`

. 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.- family
an optional

`family`

specification for a generalized linear model (`glm`

).- data
an optional data frame containing the variables named in

`formula`

. By default the variables are taken from the environment from which`lmer`

is called. See Details.- subset
an optional expression indicating the subset of the rows of

`data`

that 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.- weights
an optional vector of ‘prior weights’ to be used in the fitting process. Should be

`NULL`

or a numeric vector.- na.action
a function that indicates what should happen when the data contain

`NA`

s. The default action (`na.omit`

, inherited from the ‘factory fresh’ value of`getOption("na.action")`

) strips any observations with any missing values in any variables.- offset
this can be used to specify an

*a priori*known component to be included in the linear predictor during fitting. This should be`NULL`

or a numeric vector of length equal to the number of cases. One or more`offset`

terms can be included in the formula instead or as well, and if more than one is specified their sum is used. See`model.offset`

.- 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,

`lmer()`

, ones).- warn
indicating if errors in the single fits should signal a “summary”

`warning`

.- ...
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*). If`data`

is omitted`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 1.1-16, 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.

`lmList4`

```
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
```

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