R: Imputation via K-Nearest Neighbors

step_knnimpute {recipes} | R Documentation

## Imputation via K-Nearest Neighbors

### Description

`step_knnimpute` creates a specification of a recipe step that will impute missing data using nearest neighbors.

### Usage

    step_knnimpute(recipe, ..., role = NA, trained = FALSE, K = 5,
      impute_with = imp_vars(all_predictors()), ref_data = NULL,
      columns = NULL, skip = FALSE)

    ## S3 method for class 'step_knnimpute'
    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 variables. For `step_knnimpute`, this indicates the variables to be imputed. When used with `imp_vars`, the dots indicates which variables are used to predict the missing data in each variable. 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.
`K` | The number of neighbors.
`impute_with` | A call to `imp_vars` to specify which variables are used to impute the variables that can include specific variable names separated by commas or different selectors (see `selections()`). If a column is included in both lists to be imputed and to be an imputation predictor, it will be removed from the latter and not used to impute itself.
`ref_data` | A tibble of data that will reflect the data preprocessing done up to the point of this imputation step. This is `NULL` until the step is trained by `prep.recipe()`.
`columns` | The column names that will be imputed and used for imputation. This is `NULL` until the step is trained 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_knnimpute` object.

### Details

The step uses the training set to impute any other data sets. The only distance function available is Gower's distance which can be used for mixtures of nominal and numeric data.

Once the nearest neighbors are determined, the mode is used to predictor nominal variables and the mean is used for numeric data.

Note that if a variable that is to be imputed is also in `impute_with`, this variable will be ignored.

It is possible that missing values will still occur after imputation if a large majority (or all) of the imputing variables are also missing.

### 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 for imputation), `predictors` (those variables used to impute), and `K`.

### References

Gower, C. (1971) "A general coefficient of similarity and some of its properties," Biometrics, 857-871.

### Examples

    library(recipes)
    data(biomass)

    biomass_tr <- biomass[biomass$dataset == "Training", ]
    biomass_te <- biomass[biomass$dataset == "Testing", ]
    biomass_te_whole <- biomass_te

    # induce some missing data at random
    set.seed(9039)
    carb_missing <- sample(1:nrow(biomass_te), 3)
    nitro_missing <- sample(1:nrow(biomass_te), 3)

    biomass_te$carbon[carb_missing] <- NA
    biomass_te$nitrogen[nitro_missing] <- NA

    rec <- recipe(HHV ~ carbon + hydrogen + oxygen + nitrogen + sulfur,
                  data = biomass_tr)

    ratio_recipe <- rec %>%
      step_knnimpute(all_predictors(), K = 3)
    ratio_recipe2 <- prep(ratio_recipe, training = biomass_tr)
    imputed <- bake(ratio_recipe2, biomass_te)

    # how well did it work?
    summary(biomass_te_whole$carbon)
    cbind(before = biomass_te_whole$carbon[carb_missing],
          after = imputed$carbon[carb_missing])

    summary(biomass_te_whole$nitrogen)
    cbind(before = biomass_te_whole$nitrogen[nitro_missing],
          after = imputed$nitrogen[nitro_missing])

    tidy(ratio_recipe, number = 1)
    tidy(ratio_recipe2, number = 1)

[Package recipes version 0.1.2 Index]