step_scale
creates a specification of a recipe
step that will normalize numeric data to have a standard
deviation of one.
step_scale( recipe, ..., role = NA, trained = FALSE, sds = NULL, factor = 1, na_rm = TRUE, skip = FALSE, id = rand_id("scale") ) # S3 method for step_scale 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. |
sds | A named numeric vector of standard deviations. This
is |
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 |
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 |
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
standard deviations).
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.
Gelman, A. (2007) "Scaling regression inputs by dividing by two standard deviations." Unpublished. Source: http://www.stat.columbia.edu/~gelman/research/unpublished/standardizing.pdf.
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 10.750transformed_te#> # A tibble: 80 x 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#> # A tibble: 2 x 3 #> terms value id #> <chr> <dbl> <chr> #> 1 carbon NA scale_FYa4l #> 2 hydrogen NA scale_FYa4l#> # A tibble: 2 x 3 #> terms value id #> <chr> <dbl> <chr> #> 1 carbon 10.4 scale_FYa4l #> 2 hydrogen 1.20 scale_FYa4l