| model.frame {base} | R Documentation |
model.frame (a generic function) and its methods return a
data.frame with the variables needed to use
formula and any ... arguments.
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, ...)
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. |
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.
A data.frame containing the variables used in
formula plus those specified ....
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.
model.matrix for the “design matrix”,
formula for formulas and
expand.model.frame for model.frame manipulation.
data(cars) data.class(model.frame(dist ~ speed, data = cars))