step_bs
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, deg_free = NULL, degree = 3, objects = NULL, options = list(), skip = FALSE, id = rand_id("bs") ) # S3 method for 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. |
deg_free | The degrees of freedom. |
degree | Degree of polynomial spline (integer). |
objects | A list of |
options | A list of options for |
skip | A logical. Should the step be skipped when the
recipe is baked by |
id | A character string that is unique to this step to identify it. |
x | A |
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_bs
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
, 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.
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_bs(carbon, hydrogen) with_splines <- prep(with_splines, training = biomass_tr) expanded <- bake(with_splines, biomass_te) expanded#> # A tibble: 80 x 10 #> oxygen nitrogen sulfur HHV carbon_bs_1 carbon_bs_2 carbon_bs_3 #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 47.2 0.3 0.22 18.3 0.437 0.273 0.0568 #> 2 48.1 2.85 0.34 17.6 0.444 0.236 0.0417 #> 3 49.1 2.4 0.3 17.2 0.444 0.229 0.0394 #> 4 37.3 1.8 0.5 18.9 0.437 0.273 0.0571 #> 5 42.8 0.2 0 20.5 0.427 0.301 0.0707 #> 6 41.7 0.7 0.2 18.5 0.442 0.248 0.0465 #> 7 54.1 1.19 0.51 15.1 0.440 0.184 0.0256 #> 8 33.8 0.95 0.2 16.2 0.444 0.222 0.0369 #> 9 31.1 0.14 4.9 11.1 0.359 0.0771 0.00552 #> 10 23.7 4.63 1.05 10.8 0.338 0.0643 0.00408 #> # … with 70 more rows, and 3 more variables: hydrogen_bs_1 <dbl>, #> # hydrogen_bs_2 <dbl>, hydrogen_bs_3 <dbl>