step_invlogit creates a specification of a recipe step that will transform the data from real values to be between zero and one.

step_invlogit(
recipe,
...,
role = NA,
trained = FALSE,
columns = NULL,
skip = FALSE,
id = rand_id("invlogit")
)

# S3 method for step_invlogit
tidy(x, ...)

## 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 which variables are affected by the step. See selections() for more details. For the tidy method, these are not currently used. Not used by this step since no new variables are created. A logical to indicate if the quantities for preprocessing have been estimated. A character string of variable names that will be populated (eventually) by the terms argument. A logical. Should the step be skipped when the recipe is baked by bake.recipe()? While all operations are baked when prep.recipe() 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. A step_invlogit object.

## Value

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 which is the columns that will be affected.

## Details

The inverse logit transformation takes values on the real line and translates them to be between zero and one using the function f(x) = 1/(1+exp(-x)).

step_logit() step_log() step_sqrt() step_hyperbolic() recipe() prep.recipe() bake.recipe()

## 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)

ilogit_trans <- rec  %>%
step_center(carbon, hydrogen) %>%
step_scale(carbon, hydrogen) %>%
step_invlogit(carbon, hydrogen)

ilogit_obj <- prep(ilogit_trans, training = biomass_tr)

transformed_te <- bake(ilogit_obj, biomass_te)
plot(biomass_te$carbon, transformed_te$carbon)