model.frame {stats} | 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, ...) ## S3 method for class 'lm': model.frame(formula, ...) get_all_vars(formula, data, ...)

`formula` |
a model `formula` or `terms`
object or an R object. |

`data` |
a data.frame, list or environment (or object
coercible by `as.data.frame` to a data.frame),
containing the variables in `formula` . Neither a matrix nor an
array will be accepted. |

`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 `NA` s 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` . Another possible value is `NULL` . |

`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 `data` , `na.action` ,
`subset` . Any additional arguments such as `offset` and
`weights` which reach the default method are used to create
further columns in the model frame, with parenthesised names such as
`"(offset)"` . |

Exactly what happens depends on the class and attributes of the object
`formula`

. If this is an object of fitted-model class such as
`"lm"`

, the method will either returned the saved model frame
used when fitting the model (if any, often selected by argument
`model = TRUE`

) or pass the call used when fitting on to the
default method. The default method itself can cope with rather
standard model objects such as those of class
`"lqs"`

from package **MASS** if no other
arguments are supplied.

The rest of this section applies only to the default method.

If either `formula`

or `data`

is already a model frame (a
data frame with a `"terms"`

attribute and the other is missing,
the model frame is returned. Unless `formula`

is a terms object,
`as.formula`

and then `terms`

is called on it. (If you wish
to use the `keep.order`

argument of `terms.formula`

, pass a
terms object rather than a formula.)

Row names for the model frame are taken from the `data`

argument
if present, then from the names of the response in the formula (or
rownames if it is a matrix), if there is one.

All the variables in `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) and collected into a data frame. Then the `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.

Unless `na.action = NULL`

, time-series attributes will be removed
from the variables found (since they will be wrong if `NA`

s are
removed).

Note that *all* the variables in the formula are included in the
data frame, even those preceded by `-`

.

Only variables whose type is raw, logical, integer, real, complex or character can be included in a model frame: this includes classed variables such as factors (whose underlying type is integer), but excludes lists.

`get_all_vars`

returns a code{data.frame} containing the
variables used in `formula`

plus those specified `...`

.
Unlike `model.frame.default`

, it returns the input variables and
not those resulting from function calls in `formula`

.

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

[Package *stats* version 2.5.0 Index]