lmList {lme4} | R Documentation

## Fit List of lm or glm Objects with a Common Model

### Description

Fit a list of `lm` or `glm` objects with a common model for different subgroups of the data.

### Usage

    lmList(formula, data, family, subset, weights, na.action,
           offset, pool = !isGLM || .hasScale(family2char(family)),
           warn = TRUE, ...)

### Arguments

`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.

### Details

- 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 1.1-16, 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.

### Value

an object of `class` `lmList4` (see there, notably for the `methods` defined).

### See Also

`lmList4`

### Examples

    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

[Package lme4 version 1.1-21 Index]