step_percentile
creates a specification of a recipe step that
replaces the value of a variable with its percentile from the training set.
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
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.
- trained
A logical to indicate if the quantities for preprocessing have been estimated.
- ref_dist
The computed percentiles is stored here once this preprocessing step has be trained by
prep()
.- options
A named list of options to pass to
stats::quantile()
. See Details for more information.- 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 = TRUE
as 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.
See also
Other individual transformation steps:
step_BoxCox()
,
step_YeoJohnson()
,
step_bs()
,
step_harmonic()
,
step_hyperbolic()
,
step_inverse()
,
step_invlogit()
,
step_logit()
,
step_log()
,
step_mutate()
,
step_ns()
,
step_poly()
,
step_relu()
,
step_sqrt()
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) %>%
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
#> # … with 70 more rows
tidy(rec, 1)
#> # A tibble: 1 × 4
#> terms value percentile id
#> <chr> <dbl> <dbl> <chr>
#> 1 carbon NA NA percentile_CoF67
tidy(prepped_rec, 1)
#> # A tibble: 101 × 4
#> term value percentile id
#> <chr> <dbl> <dbl> <chr>
#> 1 carbon 14.6 0 percentile_CoF67
#> 2 carbon 25.9 1 percentile_CoF67
#> 3 carbon 28.4 2 percentile_CoF67
#> 4 carbon 31.6 3 percentile_CoF67
#> 5 carbon 35.1 4 percentile_CoF67
#> 6 carbon 35.9 5 percentile_CoF67
#> 7 carbon 37.5 6 percentile_CoF67
#> 8 carbon 38.3 7 percentile_CoF67
#> 9 carbon 38.9 8 percentile_CoF67
#> 10 carbon 39.6 9 percentile_CoF67
#> # … with 91 more rows