Impute numeric data below the threshold of measurementSource:
step_impute_lower 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.
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.
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
NULLuntil computed by
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 = TRUEas 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.
step_impute_lower 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.
recipes 0.1.16, this function name changed from
tidy() this step, a tibble with columns
terms (the selectors or variables selected) and
value for the
estimated threshold is returned.
library(recipes) data(biomass, package = "modeldata") ## 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_impute_lower(carbon, hydrogen) tidy(impute_rec, number = 1) #> # A tibble: 2 × 3 #> terms value id #> <chr> <dbl> <chr> #> 1 carbon NA impute_lower_b4CM3 #> 2 hydrogen NA impute_lower_b4CM3 impute_rec <- prep(impute_rec, training = biomass_tr) tidy(impute_rec, number = 1) #> # A tibble: 2 × 3 #> terms value id #> <chr> <dbl> <chr> #> 1 carbon 40 impute_lower_b4CM3 #> 2 hydrogen 5 impute_lower_b4CM3 transformed_te <- bake(impute_rec, biomass_te) plot(transformed_te$carbon, biomass_te$carbon, ylab = "pre-imputation", xlab = "imputed" )