model.frame (a generic function) and its methods return a
data.frame with the variables needed to use
formula and any
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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, ...)
a data.frame, list or environment (or object
a specification of the rows to be used: defaults to all
rows. This can be any valid indexing vector (see
should factors have unused levels dropped?
a named list of character vectors giving the full set of levels to be assumed for each factor.
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.
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
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
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
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
xlev arguments: if
xlev specifies a factor and a
character variable is found, it is converted to a factor (as from R
na.action = NULL, time-series attributes will be removed
from the variables found (since they will be wrong if
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.
model.frame.default, it returns the input variables and
not those resulting from function calls in
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
"na.action" giving information on the handling of
NAs (which will not be present if no special handling was done,
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.
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