model.frame
(a generic function) and its methods return a
data.frame
with the variables needed to use
formula
and any ...
arguments.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17  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 
data 
a data.frame, list or environment (or object
coercible by 
subset 
a specification of the rows to be used: defaults to all
rows. This can be any valid indexing vector (see

na.action 
how 
drop.unused.levels 
should factors have unused levels dropped?
Defaults to 
xlev 
a named list of character vectors giving the full set of levels to be assumed for each factor. 
... 
for For 
Exactly what happens depends on the class and attributes of the object
formula
. If this is an object of fittedmodel 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).
Unless na.action = NULL
, timeseries 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 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
.
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
NA
s (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.
model.matrix
for the ‘design matrix’,
formula
for formulas and
expand.model.frame
for model.frame manipulation.
1 2 3 4 5 6 7  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)

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