step_poly
creates a specification of a recipe
step that will create new columns that are basis expansions of
variables using orthogonal polynomials.
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 variables for this step. See
selections()
for more details.- 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
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 includex
,degree
, orsimple
. Note that the optionraw = TRUE
will produce the regular polynomial values (not orthogonalized).- skip
A logical. Should the step be skipped when the recipe is baked by
bake()
? While all operations are baked whenprep()
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 usingskip = TRUE
as it may affect the computations for subsequent operations.- id
A character string that is unique to this step to identify it.
Value
An updated version of recipe
with the new step added to the
sequence of any existing operations.
Details
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.
Tidying
When you tidy()
this step, a tibble with columns
terms
(the columns that will be affected) and degree
is returned.
See also
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()
Examples
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_p…¹ hydrog…² hydrog…³
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 47.2 0.3 0.22 18.3 -0.00903 -0.0124 0.00826 -0.0137
#> 2 48.1 2.85 0.34 17.6 -0.0230 0.00403 0.00160 -0.0189
#> 3 49.1 2.4 0.3 17.2 -0.0255 0.00734 0.00160 -0.0189
#> 4 37.3 1.8 0.5 18.9 -0.00880 -0.0126 0.0251 0.00301
#> 5 42.8 0.2 0 20.5 0.00183 -0.0226 0.0337 0.0136
#> 6 41.7 0.7 0.2 18.5 -0.0183 -0.00195 0.00160 -0.0189
#> 7 54.1 1.19 0.51 15.1 -0.0424 0.0331 -0.00897 -0.0255
#> 8 33.8 0.95 0.2 16.2 -0.0282 0.0111 -0.0313 -0.0326
#> 9 31.1 0.14 4.9 11.1 -0.0863 0.125 -0.0415 -0.0329
#> 10 23.7 4.63 1.05 10.8 -0.0926 0.142 -0.0662 -0.0256
#> # … with 70 more rows, and abbreviated variable names ¹carbon_poly_2,
#> # ²hydrogen_poly_1, ³hydrogen_poly_2
tidy(quadratic, number = 1)
#> # A tibble: 2 × 3
#> terms degree id
#> <chr> <int> <chr>
#> 1 carbon 2 poly_R8bgI
#> 2 hydrogen 2 poly_R8bgI