step_center() creates a specification of a recipe step that will
normalize numeric data to have a mean of zero.
Usage
step_center(
recipe,
...,
role = NA,
trained = FALSE,
means = NULL,
na_rm = TRUE,
skip = FALSE,
id = rand_id("center")
)Arguments
- 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
selections()for more details.- 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
NULLuntil computed byprep().- na_rm
A logical value indicating whether
NAvalues should be removed during computations.- skip
A logical. Should the step be skipped when the recipe is baked by
bake()? While all operations are baked whenprep()is run, some operations may not be able to be conducted on new data (e.g. processing the outcome variable(s)). Care should be taken when usingskip = TRUEas it may affect the computations for subsequent operations.- id
A character string that is unique to this step to identify it.
Value
An updated version of recipe with the new step added to the
sequence of any existing operations.
Details
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.
Tidying
When you tidy() this step, a tibble with columns
terms (the selectors or variables selected) and value (the means)
is returned.
Case weights
This step performs an unsupervised operation that can utilize case weights.
As a result, case weights are only used with frequency weights. For more
information, see the documentation in case_weights and the examples on
tidymodels.org.
See also
Other normalization steps:
step_normalize(),
step_range(),
step_scale()
Examples
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
)
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)]
#> carbon hydrogen oxygen nitrogen sulfur HHV
#> 15 46.35 5.67 47.20 0.30 0.22 18.275
#> 20 43.25 5.50 48.06 2.85 0.34 17.560
#> 26 42.70 5.50 49.10 2.40 0.30 17.173
#> 31 46.40 6.10 37.30 1.80 0.50 18.851
#> 36 48.76 6.32 42.77 0.20 0.00 20.547
#> 41 44.30 5.50 41.70 0.70 0.20 18.467
#> 46 38.94 5.23 54.13 1.19 0.51 15.095
#> 51 42.10 4.66 33.80 0.95 0.20 16.240
#> 55 29.20 4.40 31.10 0.14 4.90 11.147
#> 65 27.80 3.77 23.69 4.63 1.05 10.750
transformed_te
#> # A tibble: 80 × 6
#> carbon hydrogen oxygen nitrogen sulfur HHV
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 -2.00 5.67 8.68 -0.775 0.22 18.3
#> 2 -5.10 5.5 9.54 1.78 0.34 17.6
#> 3 -5.65 5.5 10.6 1.33 0.3 17.2
#> 4 -1.95 6.1 -1.22 0.725 0.5 18.9
#> 5 0.406 6.32 4.25 -0.875 0 20.5
#> 6 -4.05 5.5 3.18 -0.375 0.2 18.5
#> 7 -9.41 5.23 15.6 0.115 0.51 15.1
#> 8 -6.25 4.66 -4.72 -0.125 0.2 16.2
#> 9 -19.2 4.4 -7.42 -0.935 4.9 11.1
#> 10 -20.6 3.77 -14.8 3.56 1.05 10.8
#> # ℹ 70 more rows
tidy(center_trans, number = 1)
#> # A tibble: 3 × 3
#> terms value id
#> <chr> <dbl> <chr>
#> 1 "carbon" NA center_6Jp74
#> 2 "contains(\"gen\")" NA center_6Jp74
#> 3 "-hydrogen" NA center_6Jp74
tidy(center_obj, number = 1)
#> # A tibble: 3 × 3
#> terms value id
#> <chr> <dbl> <chr>
#> 1 carbon 48.4 center_6Jp74
#> 2 oxygen 38.5 center_6Jp74
#> 3 nitrogen 1.07 center_6Jp74