Orthogonal Polynomial Basis Functions

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")
)

# S3 method for step_poly
tidy(x, ...)

Arguments

recipe	A recipe object. The step will be added to the sequence of operations for this recipe.
...	One or more selector functions to choose which variables are affected by the step. See selections() for more details. For the tidy method, these are not currently used.
role	For model terms created by this step, what analysis role should they be assigned?. By default, the function assumes that the new columns created 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 stats::poly() objects created once the step has been trained.
degree	The polynomial degree (an integer).
options	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).
skip	A logical. Should the step be skipped when the recipe is baked by bake.recipe()? While all operations are baked when prep.recipe() 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
id	A character string that is unique to this step to identify it.
x	A step_poly object.

Value

An updated version of recipe with the new step added to the sequence of existing steps (if any). For the tidy method, a tibble with columns terms (the columns that will be affected) and degree.

Details

step_poly can 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.

See also

step_ns() recipe() prep.recipe() bake.recipe()

Examples

library(modeldata)
data(biomass)

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 x 8
#>    oxygen nitrogen sulfur   HHV carbon_poly_1 carbon_poly_2 hydrogen_poly_1
#>     <dbl>    <dbl>  <dbl> <dbl>         <dbl>         <dbl>           <dbl>
#>  1   47.2     0.3    0.22  18.3      -0.00903      -0.0124          0.00826
#>  2   48.1     2.85   0.34  17.6      -0.0230        0.00403         0.00160
#>  3   49.1     2.4    0.3   17.2      -0.0255        0.00734         0.00160
#>  4   37.3     1.8    0.5   18.9      -0.00880      -0.0126          0.0251 
#>  5   42.8     0.2    0     20.5       0.00183      -0.0226          0.0337 
#>  6   41.7     0.7    0.2   18.5      -0.0183       -0.00195         0.00160
#>  7   54.1     1.19   0.51  15.1      -0.0424        0.0331         -0.00897
#>  8   33.8     0.95   0.2   16.2      -0.0282        0.0111         -0.0313 
#>  9   31.1     0.14   4.9   11.1      -0.0863        0.125          -0.0415 
#> 10   23.7     4.63   1.05  10.8      -0.0926        0.142          -0.0662 
#> # … with 70 more rows, and 1 more variable: hydrogen_poly_2 <dbl>

tidy(quadratic, number = 1)
#> # A tibble: 2 x 3
#>   terms    degree id        
#>   <chr>     <int> <chr>     
#> 1 carbon        2 poly_q9QfG
#> 2 hydrogen      2 poly_q9QfG