recipes (version 1.0.0)

step_ns: Natural Spline Basis Functions

Description

step_ns creates a specification of a recipe step that will create new columns that are basis expansions of variables using natural splines.

Usage

step_ns(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  objects = NULL,
  deg_free = 2,
  options = list(),
  skip = FALSE,
  id = rand_id("ns")
)

Value

An updated version of recipe with the new step added to the sequence of any existing operations.

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 splines::ns() objects created once the step has been trained.

deg_free

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.

options

A list of options for splines::ns() which should not include x or df.

skip

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.

id

A character string that is unique to this step to identify it.

Tidying

When you tidy() this step, a tibble with column terms (the columns that will be affected) is returned.

Case weights

The underlying operation does not allow for case weights.

Details

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 knot 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.

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_percentile(), step_poly(), 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
)

with_splines <- rec %>%
  step_ns(carbon, hydrogen)
with_splines <- prep(with_splines, training = biomass_tr)

expanded <- bake(with_splines, biomass_te)
expanded