Extracting the Model Frame from a Formula or Fit

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, …)

# S3 method for default model.frame(formula, data = NULL, subset = NULL, na.action =, drop.unused.levels = FALSE, xlev = NULL, …)

# S3 method for aovlist model.frame(formula, data = NULL, …)

# S3 method for glm model.frame(formula, …)

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

get_all_vars(formula, data, …)

a model formula or terms object or an R object.
a data.frame, list or environment (or object coercible by to a data.frame), containing the variables in formula. Neither a matrix nor an array will be accepted.
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.
how NAs are treated. The default is first, any na.action attribute of data, second a na.action setting of options, and third if that is unset. The ‘factory-fresh’ default is na.omit. Another possible value is NULL.
should factors have unused levels dropped? Defaults to FALSE.
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 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 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). 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 . 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 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).


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.

  • model.frame
  • model.frame.default
  • model.frame.lm
  • model.frame.glm
  • model.frame.aovlist
  • get_all_vars
library(stats) data.class(model.frame(dist ~ speed, data = cars))
Documentation reproduced from package stats, version 3.3.3, License: Part of R 3.3.3

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