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.
step_lowerimpute( recipe, ..., 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
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
A logical. Should the step be skipped when the
recipe is baked by
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 existing steps (if any). For the
tidy method, a tibble with columns
selectors or variables selected) and
value for the estimated
step_lowerimpute estimates the variable minimums
from the data used in the
training argument of
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#> # A tibble: 2 x 3 #> terms value id #> <chr> <dbl> <chr> #> 1 carbon 40 lowerimpute_f9MLF #> 2 hydrogen 5 lowerimpute_f9MLFtransformed_te <- bake(impute_rec, biomass_te) plot(transformed_te$carbon, biomass_te$carbon, ylab = "pre-imputation", xlab = "imputed")