Orthogonal Polynomial Basis FunctionsSource:
step_poly() creates a specification of a recipe step that will create new
columns that are basis expansions of variables using orthogonal polynomials.
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
simple. Note that the option
raw = TRUEwill produce the regular polynomial values (not orthogonalized).
A logical to keep the original variables in the output. Defaults to
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 = TRUEas it may affect the computations for subsequent operations.
A character string that is unique to this step to identify it.
An updated version of
recipe with the new step added to the
sequence of any existing operations.
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.
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)
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 #> # A tibble: 80 × 8 #> oxygen nitrogen sulfur HHV carbon_poly_1 carbon_poly_2 #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 47.2 0.3 0.22 18.3 -0.00903 -0.0124 #> 2 48.1 2.85 0.34 17.6 -0.0230 0.00403 #> 3 49.1 2.4 0.3 17.2 -0.0255 0.00734 #> 4 37.3 1.8 0.5 18.9 -0.00880 -0.0126 #> 5 42.8 0.2 0 20.5 0.00183 -0.0226 #> 6 41.7 0.7 0.2 18.5 -0.0183 -0.00195 #> 7 54.1 1.19 0.51 15.1 -0.0424 0.0331 #> 8 33.8 0.95 0.2 16.2 -0.0282 0.0111 #> 9 31.1 0.14 4.9 11.1 -0.0863 0.125 #> 10 23.7 4.63 1.05 10.8 -0.0926 0.142 #> # ℹ 70 more rows #> # ℹ 2 more variables: hydrogen_poly_1 <dbl>, hydrogen_poly_2 <dbl> tidy(quadratic, number = 1) #> # A tibble: 2 × 3 #> terms degree id #> <chr> <int> <chr> #> 1 carbon 2 poly_R8bgI #> 2 hydrogen 2 poly_R8bgI