step_ratio() creates a specification of a recipe step that will create
one or more ratios from selected numeric variables.
step_ratio(
recipe,
...,
role = "predictor",
trained = FALSE,
denom = denom_vars(),
naming = function(numer, denom) {
make.names(paste(numer, denom, sep = "_o_"))
},
columns = NULL,
keep_original_cols = TRUE,
skip = FALSE,
id = rand_id("ratio")
)
denom_vars(...)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 will be
used in the numerator of the ratio. When used with denom_vars, the dots
indicate which variables are used in the denominator. 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.
Bare names that specifies which variables are used in the
denominator that can include specific variable names separated by commas or
different selectors (see selections()). Can also be a strings or
tidyselect for backwards compatibility If a column is included in both
lists to be numerator and denominator, it will be removed from the listing.
A function that defines the naming convention for new ratio columns.
A character string of the selected variable names. This field
is a placeholder and will be populated once prep() is used.
A logical to keep the original variables in the
output. Defaults to TRUE.
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, denom , and id:
character, the selectors or variables selected
character, name of denominator selected
character, id of this step
The underlying operation does not allow for case weights.
Other multivariate transformation steps:
step_classdist(),
step_classdist_shrunken(),
step_depth(),
step_geodist(),
step_ica(),
step_isomap(),
step_kpca(),
step_kpca_poly(),
step_kpca_rbf(),
step_mutate_at(),
step_nnmf(),
step_nnmf_sparse(),
step_pca(),
step_pls(),
step_spatialsign()
library(recipes)
data(biomass, package = "modeldata")
biomass$total <- apply(biomass[, 3:7], 1, sum)
biomass_tr <- biomass[biomass$dataset == "Training", ]
biomass_te <- biomass[biomass$dataset == "Testing", ]
rec <- recipe(HHV ~ carbon + hydrogen + oxygen + nitrogen +
sulfur + total,
data = biomass_tr
)
ratio_recipe <- rec |>
# all predictors over total
step_ratio(all_numeric_predictors(), denom = total,
keep_original_cols = FALSE)
ratio_recipe <- prep(ratio_recipe, training = biomass_tr)
ratio_data <- bake(ratio_recipe, biomass_te)
ratio_data
#> # A tibble: 80 × 6
#> HHV carbon_o_total hydrogen_o_total oxygen_o_total nitrogen_o_total
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 18.3 0.465 0.0568 0.473 0.00301
#> 2 17.6 0.432 0.055 0.481 0.0285
#> 3 17.2 0.427 0.055 0.491 0.024
#> 4 18.9 0.504 0.0662 0.405 0.0195
#> 5 20.5 0.497 0.0645 0.436 0.00204
#> 6 18.5 0.479 0.0595 0.451 0.00758
#> 7 15.1 0.389 0.0523 0.541 0.0119
#> 8 16.2 0.515 0.0570 0.414 0.0116
#> 9 11.1 0.419 0.0631 0.446 0.00201
#> 10 10.8 0.456 0.0619 0.389 0.0760
#> # ℹ 70 more rows
#> # ℹ 1 more variable: sulfur_o_total <dbl>