run-mold | R Documentation |
mold()
according to a blueprintThis is a developer facing function that is only used if you are creating
your own blueprint subclass. It is called from mold()
and dispatches off
the S3 class of the blueprint
. This gives you an opportunity to mold the
data in a way that is specific to your blueprint.
run_mold()
will be called with different arguments depending on the
interface to mold()
that is used:
XY interface:
run_mold(blueprint, x = x, y = y)
Formula interface:
run_mold(blueprint, data = data)
Additionally, the blueprint
will have been updated to contain the
formula
.
Recipe interface:
run_mold(blueprint, data = data)
Additionally, the blueprint
will have been updated to contain the
recipe
.
If you write a blueprint subclass for new_xy_blueprint()
,
new_recipe_blueprint()
, or new_formula_blueprint()
then your run_mold()
method signature must match whichever interface listed above will be used.
If you write a completely new blueprint inheriting only from
new_blueprint()
and write a new mold()
method (because you aren't using
an xy, formula, or recipe interface), then you will have full control over
how run_mold()
will be called.
run_mold(blueprint, ...)
## S3 method for class 'default_formula_blueprint'
run_mold(blueprint, ..., data, call = caller_env())
## S3 method for class 'default_recipe_blueprint'
run_mold(blueprint, ..., data, call = caller_env())
## S3 method for class 'default_xy_blueprint'
run_mold(blueprint, ..., x, y, call = caller_env())
blueprint |
A preprocessing blueprint. |
... |
Not used. Required for extensibility. |
data |
A data frame or matrix containing the outcomes and predictors. |
call |
The call used for errors and warnings. |
x |
A data frame or matrix containing the predictors. |
y |
A data frame, matrix, or vector containing the outcomes. |
run_mold()
methods return the object that is then immediately returned from
mold()
. See the return value section of mold()
to understand what the
structure of the return value should look like.
bp <- default_xy_blueprint()
outcomes <- mtcars["mpg"]
predictors <- mtcars
predictors$mpg <- NULL
run_mold(bp, x = predictors, y = outcomes)
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