Centering numeric data
step_center creates a specification of a recipe
step that will normalize numeric data to have a mean of zero.
step_center(
recipe,
...,
role = NA,
trained = FALSE,
means = NULL,
na_rm = TRUE,
skip = FALSE,
id = rand_id("center")
)
## S3 method for class 'step_center'
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 |
Not used by this step since no new variables are created. |
trained |
A logical to indicate if the quantities for preprocessing have been estimated. |
means |
A named numeric vector of means. This is
|
na_rm |
A logical value indicating whether |
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 |
Centering data means that the average of a variable is
subtracted from the data. step_center estimates the
variable means from the data used in the training
argument of prep.recipe. bake.recipe then applies
the centering to new data sets using these means.
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 (the
selectors or variables selected) and value (the means).
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)
center_trans <- rec %>%
step_center(carbon, contains("gen"), -hydrogen)
center_obj <- prep(center_trans, training = biomass_tr)
transformed_te <- bake(center_obj, biomass_te)
biomass_te[1:10, names(transformed_te)]
transformed_te
tidy(center_trans, number = 1)
tidy(center_obj, number = 1)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.