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model.frame: Extracting the Model Frame from a Formula or Fit

model.frame

R Documentation

Extracting the Model Frame from a Formula or Fit

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, ...)

## S3 method for class 'lm'
model.frame(formula, ...)

get_all_vars(formula, data, ...)

Arguments

`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`. (See additional details about how this argument interacts with data-dependent bases under ‘Details’ below.)
`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.
`...`	for `model.frame` methods, a mix of further arguments such as `data`, `na.action`, `subset` to pass to the default method. Any additional arguments (such as `offset` and `weights` or other named arguments) which reach the default method are used to create further columns in the model frame, with parenthesised names such as `"(offset)"`. For `get_all_vars`, further named columns to include in the model frame.

Details

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 return 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 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: if xlev specifies a factor and a character variable is found, it is converted to a factor (as from R 2.10.0).

Because variables in the formula are evaluated before rows are dropped based on subset, the characteristics of data-dependent bases such as orthogonal polynomials (i.e. from terms using poly) or splines will be computed based on the full data set rather than the subsetted one.

Unless na.action = NULL, time-series attributes will be removed from the variables found (since they will be wrong if NAs 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 data.frame containing the variables used in formula plus those specified in ... which are recycled to the number of data frame rows. Unlike model.frame.default, it returns the input variables and not those resulting from function calls in formula.

Value

A data.frame containing the variables used in formula plus those specified in .... It will have additional attributes, including "terms" for an object of class "terms" derived from formula, and possibly "na.action" giving information on the handling of NAs (which will not be present if no special handling was done, e.g. by na.pass).

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.

Examples

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

## get_all_vars(): new var.s are recycled (iff length matches: 50 = 2*25)
ncars <- get_all_vars(sqrt(dist) ~ I(speed/2), data = cars, newVar = 2:3)
stopifnot(is.data.frame(ncars),
          identical(cars, ncars[,names(cars)]),
          ncol(ncars) == ncol(cars) + 1)