step_ns {recipes} | R Documentation |
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, options = list(df = 2))
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 |
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 |
options |
A list of options for |
step_ns
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 df
or knot
arguments of 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.
An updated version of recipe
with the
new step added to the sequence of existing steps (if any).
step_poly
recipe
prep.recipe
bake.recipe
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