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

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

# S3 method for step_ns
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 splines::ns() objects created once the step has been trained.

deg_free

The degrees of freedom.

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.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_ns 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 which is the columns that will be affected and holiday.

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

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) 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 x 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 #> # … with 70 more rows, and 1 more variable: hydrogen_ns_2 <dbl>