recipes: step_impute_lower – R documentation

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step_impute_lower

Impute Numeric Data Below the Threshold of Measurement

Description

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.

Usage

step_impute_lower(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  threshold = NULL,
  skip = FALSE,
  id = rand_id("impute_lower")
)

step_lowerimpute(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  threshold = NULL,
  skip = FALSE,
  id = rand_id("impute_lower")
)

## S3 method for class 'step_impute_lower'
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.
`role`	Not used by this step since no new variables are created.
`trained`	A logical to indicate if the quantities for preprocessing have been estimated.
`threshold`	A named numeric vector of lower bounds. This is `NULL` until computed by `prep.recipe()`.
`skip`	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
`id`	A character string that is unique to this step to identify it.
`x`	A `step_impute_lower` object.

Details

step_impute_lower 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.

As of recipes 0.1.16, this function name changed from step_lowerimpute() to step_impute_lower().

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 (the selectors or variables selected) and value for the estimated threshold.

Examples

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_impute_lower(carbon, hydrogen)

tidy(impute_rec, number = 1)

impute_rec <- prep(impute_rec, training = biomass_tr)

tidy(impute_rec, number = 1)

transformed_te <- bake(impute_rec, biomass_te)

plot(transformed_te$carbon, biomass_te$carbon,
     ylab = "pre-imputation", xlab = "imputed")

recipes

Preprocessing Tools to Create Design Matrices

v0.1.16

MIT + file LICENSE

Authors

Max Kuhn [aut, cre], Hadley Wickham [aut], RStudio [cph]

Initial release