step_bs | R Documentation |
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(),
keep_original_cols = FALSE,
skip = FALSE,
id = rand_id("bs")
)
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 |
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. |
deg_free |
The degrees of freedom for the spline. As the degrees of freedom for a 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. |
degree |
Degree of polynomial spline (integer). |
objects |
A list of |
options |
A list of options for |
keep_original_cols |
A logical to keep the original variables in the
output. Defaults to |
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. |
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.
An updated version of recipe
with the new step added to the
sequence of any existing operations.
When you tidy()
this step, a tibble with column
terms
(the columns that will be affected) is returned.
This step has 2 tuning parameters:
deg_free
: Spline Degrees of Freedom (type: integer, default: NULL)
degree
: Polynomial Degree (type: integer, default: 3)
The underlying operation does not allow for case weights.
Other individual transformation steps:
step_BoxCox()
,
step_YeoJohnson()
,
step_harmonic()
,
step_hyperbolic()
,
step_inverse()
,
step_invlogit()
,
step_logit()
,
step_log()
,
step_mutate()
,
step_ns()
,
step_percentile()
,
step_poly()
,
step_relu()
,
step_sqrt()
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_bs(carbon, hydrogen)
with_splines <- prep(with_splines, training = biomass_tr)
expanded <- bake(with_splines, biomass_te)
expanded
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