step_ns() creates a specification of a recipe step that will create new
columns that are basis expansions of variables using natural splines.
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 splines::ns() objects created once the step has
been trained.
The degrees of freedom for the natural spline. As the degrees of freedom for a natural spline increase, more flexible and complex curves can be generated. When a single degree of freedom is used, the result is a rescaled version of the original data.
A list of options for splines::ns() which should not include
x or df.
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.
An updated version of recipe with the new step added to the
sequence of any existing operations.
step_ns() 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 df or knots arguments of
splines::ns(). The original variables are removed from the data and new
columns are added. The naming convention for the new variables is
varname_ns_1 and so on.
When you tidy() this step, a tibble is returned with
columns terms and id:
character, the selectors or variables selected
character, id of this step
This step has 1 tuning parameters:
deg_free: Spline Degrees of Freedom (type: integer, default: 2)
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_log(),
step_logit(),
step_mutate(),
step_percentile(),
step_poly(),
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
)
with_splines <- rec |>
step_ns(carbon, hydrogen)
with_splines <- prep(with_splines, training = biomass_tr)
expanded <- bake(with_splines, biomass_te)
expanded
#> # A tibble: 80 × 8
#> oxygen nitrogen sulfur HHV carbon_ns_1 carbon_ns_2 hydrogen_ns_1
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 47.2 0.3 0.22 18.3 0.524 -0.236 0.563
#> 2 48.1 2.85 0.34 17.6 0.493 -0.241 0.556
#> 3 49.1 2.4 0.3 17.2 0.487 -0.241 0.556
#> 4 37.3 1.8 0.5 18.9 0.524 -0.236 0.574
#> 5 42.8 0.2 0 20.5 0.542 -0.226 0.577
#> 6 41.7 0.7 0.2 18.5 0.504 -0.240 0.556
#> 7 54.1 1.19 0.51 15.1 0.440 -0.233 0.544
#> 8 33.8 0.95 0.2 16.2 0.480 -0.240 0.512
#> 9 31.1 0.14 4.9 11.1 0.285 -0.169 0.493
#> 10 23.7 4.63 1.05 10.8 0.260 -0.155 0.442
#> # ℹ 70 more rows
#> # ℹ 1 more variable: hydrogen_ns_2 <dbl>