step_bs {recipes} | R Documentation |
step_ns
creates a specification of a recipe step
that will create new columns that are basis expansions of
variables using B-splines.
step_bs(recipe, ..., role = "predictor", trained = FALSE, objects = NULL, options = list(df = NULL, degree = 3)) ## S3 method for class 'step_bs' tidy(x, ...)
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 |
x |
A |
step_bs
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
, degree
, or knot
arguments of
splines::bs()
. The original variables are removed
from the data and new columns are added. The naming convention
for the new variables is varname_bs_1
and so on.
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
.
step_poly()
recipe()
step_ns()
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_bs(carbon, hydrogen) with_splines <- prep(with_splines, training = biomass_tr) expanded <- bake(with_splines, biomass_te) expanded