step_poly() creates a specification of a recipe step that will create new
columns that are basis expansions of variables using orthogonal polynomials.
step_poly(
recipe,
...,
role = "predictor",
trained = FALSE,
objects = NULL,
degree = 2,
options = list(),
keep_original_cols = FALSE,
skip = FALSE,
id = rand_id("poly")
)An updated version of recipe with the new step added to the
sequence of any existing operations.
A recipe object. The step will be added to the sequence of operations for this recipe.
One or more selector functions to choose variables
for this step. See selections() for more details.
For model terms created by this step, what analysis role should they be assigned? By default, the new columns created by this step from the original variables will be used as predictors in a model.
A logical to indicate if the quantities for preprocessing have been estimated.
A list of stats::poly() objects
created once the step has been trained.
The polynomial degree (an integer).
A list of options for stats::poly()
which should not include x, degree, or simple. Note that
the option raw = TRUE will produce the regular polynomial
values (not orthogonalized).
A logical to keep the original variables in the
output. Defaults to FALSE.
A logical. Should the step be skipped when the
recipe is baked by bake()? While all operations are baked
when prep() is run, some operations may not be able to be
conducted on new data (e.g. processing the outcome variable(s)).
Care should be taken when using skip = TRUE as it may affect
the computations for subsequent operations.
A character string that is unique to this step to identify it.
When you tidy() this step, a tibble with columns
terms (the columns that will be affected) and degree is returned.
This step has 1 tuning parameters:
degree: Polynomial Degree (type: integer, default: 2)
The underlying operation does not allow for case weights.
step_poly can create new features from a single
variable that enable fitting routines to model this variable in
a nonlinear manner. The extent of the possible nonlinearity is
determined by the degree argument of
stats::poly(). The original variables are removed
from the data and new columns are added. The naming convention
for the new variables is varname_poly_1 and so on.
Other individual transformation steps:
step_BoxCox(),
step_YeoJohnson(),
step_bs(),
step_harmonic(),
step_hyperbolic(),
step_inverse(),
step_invlogit(),
step_logit(),
step_log(),
step_mutate(),
step_ns(),
step_percentile(),
step_relu(),
step_sqrt()
data(biomass, package = "modeldata")
biomass_tr <- biomass[biomass$dataset == "Training", ]
biomass_te <- biomass[biomass$dataset == "Testing", ]
rec <- recipe(
HHV ~ carbon + hydrogen + oxygen + nitrogen + sulfur,
data = biomass_tr
)
quadratic <- rec %>%
step_poly(carbon, hydrogen)
quadratic <- prep(quadratic, training = biomass_tr)
expanded <- bake(quadratic, biomass_te)
expanded
tidy(quadratic, number = 1)
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