model.frame {base}R Documentation

Extracting the “Environment” of a Model Formula

Description

model.frame (a generic function) and its methods return a data.frame with the variables needed to use formula and any ... arguments.

Usage

model.frame(formula, ...)

## Default S3 method:
model.frame(formula, data = NULL,
           subset = NULL, na.action = na.fail,
           drop.unused.levels = FALSE, xlev = NULL, ...)

## S3 method for class 'aovlist':
model.frame(formula, data = NULL, ...)

## S3 method for class 'glm':
model.frame(formula, data, na.action, ...)

## S3 method for class 'lm':
model.frame(formula, data, na.action, ...)

Arguments

formula a model formula
data data.frame, list, environment or object coercible to data.frame containing the variables in formula.
subset a specification of the rows to be used: defaults to all rows. This can be any valid indexing vector (see [.data.frame for the rows of data or if that is not supplied, a data frame made up of the variables used in formula.
na.action how NAs are treated. The default is first, any na.action attribute of data, second a na.action setting of options, and third na.fail if that is unset. The “factory-fresh” default is na.omit.
drop.unused.levels should factors have unused levels dropped? Defaults to FALSE.
xlev a named list of character vectors giving the full set of levels to be assumed for each factor.
... further arguments such as subset, offset and weights. NULL arguments are treated as missing.

Details

Variables in the formula, subset and in ... are looked for first in data and then in the environment of formula: see the help for formula() for further details.

First all the variables needed are collected into a data frame. Then subset expression is evaluated, and it is is used as a row index to the data frame. Then the na.action function is applied to the data frame (and may well add attributes). The levels of any factors in the data frame are adjusted according to the drop.unused.levels and xlev arguments.

Value

A data.frame containing the variables used in formula plus those specified ....

References

Chambers, J. M. (1992) Data for models. Chapter 3 of Statistical Models in S eds J. M. Chambers and T. J. Hastie, Wadsworth & Brooks/Cole.

See Also

model.matrix for the “design matrix”, formula for formulas and expand.model.frame for model.frame manipulation.

Examples

data(cars)
data.class(model.frame(dist ~ speed, data = cars))
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