R: Impute Numeric Data Below the Threshold of Measurement

step_lowerimpute {recipes} | R Documentation

## Impute Numeric Data Below the Threshold of Measurement

### Description

`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.

### Usage

    step_lowerimpute(recipe, ..., role = NA, trained = FALSE,
      threshold = NULL, skip = FALSE)

    ## S3 method for class 'step_lowerimpute'
    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
`x` | A `step_lowerimpute` object.

### Details

`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.

### 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)
    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)

    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,
         xlab = "pre-imputation", ylab = "imputed")

[Package recipes version 0.1.2 Index]