Construct a model frame
model_frame() is a stricter version of stats::model.frame(). There are
a number of differences, with the main being that rows are never dropped
and the return value is a list with the frame and terms separated into
two distinct objects.
model_frame(formula, data)
formula |
A formula or terms object representing the terms of the model frame. |
data |
A data frame or matrix containing the terms of |
The following explains the rationale for some of the difference in arguments
compared to stats::model.frame():
subset: Not allowed because the number of rows before and after
model_frame() has been run should always be the same.
na.action: Not allowed and is forced to "na.pass" because the
number of rows before and after model_frame() has been run should always
be the same.
drop.unused.levels: Not allowed because it seems inconsistent for
data and the result of model_frame() to ever have the same factor column
but with different levels, unless specified though original_levels. If
this is required, it should be done through a recipe step explicitly.
xlev: Not allowed because this check should have been done ahead of
time. Use scream() to check the integrity of data against a training
set if that is required.
...: Not exposed because offsets are handled separately, and
it is not necessary to pass weights here any more because rows are never
dropped (so weights don't have to be subset alongside the rest of the
design matrix). If other non-predictor columns are required, use the
"roles" features of recipes.
It is important to always use the results of model_frame() with
model_matrix() rather than stats::model.matrix() because the tibble
in the result of model_frame() does not have a terms object attached.
If model.matrix(<terms>, <tibble>) is called directly, then a call to
model.frame() will be made automatically, which can give faulty results.
A named list with two elements:
"data": A tibble containing the model frame.
"terms": A terms object containing the terms for the model frame.
# --------------------------------------------------------------------------- # Example usage framed <- model_frame(Species ~ Sepal.Width, iris) framed$data framed$terms # --------------------------------------------------------------------------- # Missing values never result in dropped rows iris2 <- iris iris2$Sepal.Width[1] <- NA framed2 <- model_frame(Species ~ Sepal.Width, iris2) head(framed2$data) nrow(framed2$data) == nrow(iris2)
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