nlme (version 3.1-66)

lmList: List of lm Objects with a Common Model

Description

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.

Usage

lmList(object, data, level, subset, na.action, pool)
## S3 method for class 'lmList':
update(object, formula., \dots, evaluate = TRUE)
## S3 method for class 'lmList':
print(x, pool, \dots)

Arguments

object

For 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

formula

(used in update.lmList only) a two-sided linear formula with the common model for the individuals lm fits.

formula.

Changes to the formula -- see update.formula for details.

data

a data frame in which to interpret the variables named in object.

level

an optional integer specifying the level of grouping to be used when multiple nested levels of grouping are present.

subset

an optional expression indicating which subset of the rows of 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

na.action

a function that indicates what should happen when the data contain NAs. The default action (na.fail) causes lmList to print an error message and terminate if there are any incomplete observations.

pool

an optional logical value indicating whether a pooled estimate of the residual standard error should be used in calculations of standard deviations or standard errors for summaries.

x

an object inheriting from class lmList to be printed.

...

some methods for this generic require additional arguments. None are used in this method.

evaluate

If TRUE evaluate the new call else return the call.

Value

• a list of 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.

See Also

lm, lme.lmList.

Examples

fm1 <- lmList(distance ~ age | Subject, Orthodont)
summary(fm1)