step_percentile() creates a specification of a recipe step that replaces
the value of a variable with its percentile from the training set.
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.
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.
A logical to indicate if the quantities for preprocessing have been estimated.
The computed percentiles is stored here once this
preprocessing step has be trained by prep().
A named list of options to pass to stats::quantile(). See
Details for more information.
A character, describing how interpolation is to take place
outside the interval [min(x), max(x)]. none means nothing will happen
and values outside the range will be NA. lower means that new values
less than min(x) will be given the value 0. upper means that new
values larger than max(x) will be given the value 1. both will handle
both cases. Defaults to none.
A logical. Should the step be skipped when the recipe is baked by
bake()? While all operations are baked when prep() 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 using skip = TRUE as it
may affect the computations for subsequent operations.
A character string that is unique to this step to identify it.
An updated version of recipe with the new step added to the
sequence of any existing operations.
When you tidy() this step, a tibble is returned with
columns terms, value, percentile , and id:
character, the selectors or variables selected
numeric, the value at the percentile
numeric, the percentile as a percentage
character, id of this step
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.
Other individual transformation steps:
step_BoxCox(),
step_YeoJohnson(),
step_bs(),
step_harmonic(),
step_hyperbolic(),
step_inverse(),
step_invlogit(),
step_log(),
step_logit(),
step_mutate(),
step_ns(),
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
) |>
step_percentile(carbon)
prepped_rec <- prep(rec)
prepped_rec |>
bake(biomass_te)
#> # A tibble: 80 × 6
#> carbon hydrogen oxygen nitrogen sulfur HHV
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0.421 5.67 47.2 0.3 0.22 18.3
#> 2 0.18 5.5 48.1 2.85 0.34 17.6
#> 3 0.156 5.5 49.1 2.4 0.3 17.2
#> 4 0.423 6.1 37.3 1.8 0.5 18.9
#> 5 0.666 6.32 42.8 0.2 0 20.5
#> 6 0.218 5.5 41.7 0.7 0.2 18.5
#> 7 0.0803 5.23 54.1 1.19 0.51 15.1
#> 8 0.139 4.66 33.8 0.95 0.2 16.2
#> 9 0.0226 4.4 31.1 0.14 4.9 11.1
#> 10 0.0178 3.77 23.7 4.63 1.05 10.8
#> # ℹ 70 more rows
tidy(rec, 1)
#> # A tibble: 1 × 4
#> terms value percentile id
#> <chr> <dbl> <dbl> <chr>
#> 1 carbon NA NA percentile_dwNDP
tidy(prepped_rec, 1)
#> # A tibble: 101 × 4
#> terms value percentile id
#> <chr> <dbl> <dbl> <chr>
#> 1 carbon 14.6 0 percentile_dwNDP
#> 2 carbon 25.9 1 percentile_dwNDP
#> 3 carbon 28.4 2 percentile_dwNDP
#> 4 carbon 31.6 3 percentile_dwNDP
#> 5 carbon 35.1 4 percentile_dwNDP
#> 6 carbon 35.9 5 percentile_dwNDP
#> 7 carbon 37.5 6 percentile_dwNDP
#> 8 carbon 38.3 7 percentile_dwNDP
#> 9 carbon 38.9 8 percentile_dwNDP
#> 10 carbon 39.6 9 percentile_dwNDP
#> # ℹ 91 more rows