step_lowerimpute creates a specification of a recipe step designed for cases where the non-negative numeric data cannot be measured below a known value. In these cases, one method for imputing the data is to substitute the truncated value by a random uniform number between zero and the truncation point.

  role = NA,
  trained = FALSE,
  threshold = NULL,
  skip = FALSE,
  id = rand_id("lowerimpute")

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



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 named numeric vector of lower bounds. This is NULL until computed by prep.recipe().


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_lowerimpute object.


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 for the estimated threshold.


step_lowerimpute estimates the variable minimums from the data used in the training argument of prep.recipe. bake.recipe then simulates a value for any data at the minimum with a random uniform value between zero and the minimum.


library(recipes) library(modeldata) data(biomass) ## Truncate some values to emulate what a lower limit of ## the measurement system might look like biomass$carbon <- ifelse(biomass$carbon > 40, biomass$carbon, 40) biomass$hydrogen <- ifelse(biomass$hydrogen > 5, biomass$carbon, 5) biomass_tr <- biomass[biomass$dataset == "Training",] biomass_te <- biomass[biomass$dataset == "Testing",] rec <- recipe(HHV ~ carbon + hydrogen + oxygen + nitrogen + sulfur, data = biomass_tr) impute_rec <- rec %>% step_lowerimpute(carbon, hydrogen) tidy(impute_rec, number = 1)
#> # A tibble: 2 x 3 #> terms value id #> <chr> <dbl> <chr> #> 1 carbon NA lowerimpute_f9MLF #> 2 hydrogen NA lowerimpute_f9MLF
impute_rec <- prep(impute_rec, training = biomass_tr) tidy(impute_rec, number = 1)
#> # A tibble: 2 x 3 #> terms value id #> <chr> <dbl> <chr> #> 1 carbon 40 lowerimpute_f9MLF #> 2 hydrogen 5 lowerimpute_f9MLF
transformed_te <- bake(impute_rec, biomass_te) plot(transformed_te$carbon, biomass_te$carbon, ylab = "pre-imputation", xlab = "imputed")