step_scale creates a specification of a recipe
step that will normalize numeric data to have a standard
deviation of one.
Usage
step_scale(
recipe,
...,
role = NA,
trained = FALSE,
sds = NULL,
factor = 1,
na_rm = TRUE,
skip = FALSE,
id = rand_id("scale")
)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.
- sds
A named numeric vector of standard deviations. This is
NULLuntil computed byprep().- factor
A numeric value of either 1 or 2 that scales the numeric inputs by one or two standard deviations. By dividing by two standard deviations, the coefficients attached to continuous predictors can be interpreted the same way as with binary inputs. Defaults to
1. More in reference below.- na_rm
A logical value indicating whether
NAvalues should be removed when computing the standard deviation.- 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
Scaling data means that the standard deviation of a
variable is divided out of the data. step_scale estimates
the variable standard deviations from the data used in the
training argument of prep.recipe.
bake.recipe then applies the scaling to new data sets
using these standard deviations.
Tidying
When you tidy() this step, a tibble with columns
terms (the selectors or variables selected) and value (the
standard deviations) is returned.
References
Gelman, A. (2007) "Scaling regression inputs by dividing by two standard deviations." Unpublished. Source: http://www.stat.columbia.edu/~gelman/research/unpublished/standardizing.pdf.
See also
Other normalization steps:
step_center(),
step_normalize(),
step_range()
Examples
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)
scaled_trans <- rec %>%
step_scale(carbon, hydrogen)
scaled_obj <- prep(scaled_trans, training = biomass_tr)
transformed_te <- bake(scaled_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 4.45 4.74 47.2 0.3 0.22 18.3
#> 2 4.16 4.60 48.1 2.85 0.34 17.6
#> 3 4.10 4.60 49.1 2.4 0.3 17.2
#> 4 4.46 5.10 37.3 1.8 0.5 18.9
#> 5 4.68 5.28 42.8 0.2 0 20.5
#> 6 4.26 4.60 41.7 0.7 0.2 18.5
#> 7 3.74 4.37 54.1 1.19 0.51 15.1
#> 8 4.04 3.89 33.8 0.95 0.2 16.2
#> 9 2.81 3.68 31.1 0.14 4.9 11.1
#> 10 2.67 3.15 23.7 4.63 1.05 10.8
#> # … with 70 more rows
tidy(scaled_trans, number = 1)
#> # A tibble: 2 × 3
#> terms value id
#> <chr> <dbl> <chr>
#> 1 carbon NA scale_vytZo
#> 2 hydrogen NA scale_vytZo
tidy(scaled_obj, number = 1)
#> # A tibble: 2 × 3
#> terms value id
#> <chr> <dbl> <chr>
#> 1 carbon 10.4 scale_vytZo
#> 2 hydrogen 1.20 scale_vytZo