step_poly {recipes} | R Documentation |
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(), skip = FALSE, id = rand_id("poly") )
recipe |
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 |
role |
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. |
trained |
A logical to indicate if the quantities for preprocessing have been estimated. |
objects |
A list of |
degree |
The polynomial degree (an integer). |
options |
A list of options for |
skip |
A logical. Should the step be skipped when the
recipe is baked by |
id |
A character string that is unique to this step to identify it. |
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.
An updated version of recipe
with the new step added to the
sequence of any existing operations.
When you tidy()
this step, a tibble with columns
terms
(the columns that will be affected) and degree
is returned.
The underlying operation does not allow for case weights.
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)