step_impute_knn {recipes}

Impute via k-nearest neighbors

Description

step_impute_knn() creates a specification of a recipe step that will impute missing data using nearest neighbors.

Usage

step_impute_knn(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  neighbors = 5,
  impute_with = imp_vars(all_predictors()),
  options = list(nthread = 1, eps = 1e-08),
  ref_data = NULL,
  columns = NULL,
  skip = FALSE,
  id = rand_id("impute_knn")
)

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 to be imputed. When used with imp_vars, these dots indicate which variables are used to predict the missing data in each variable. See selections() for more details.

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.

neighbors

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.

options

A named list of options to pass to gower::gower_topn(). Available options are currently nthread and eps.

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().

columns

A character string of the selected variable names. This field is a placeholder and will be populated once prep() is used.

skip

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 = TRUE as it may affect the computations for subsequent operations.

id

A character string that is unique to this step to identify it.

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 the underlying data are integer, the mean will be converted to an integer too.

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.

As of recipes 0.1.16, this function name changed from step_knnimpute() to step_impute_knn().

Value

An updated version of recipe with the new step added to the sequence of any existing operations.

Tidying

When you tidy() this step, a tibble is returned with columns terms, predictors, neighbors , and id:

terms

character, the selectors or variables selected

predictors

character, selected predictors used to impute

neighbors

integer, number of neighbors

id

character, id of this step

Tuning Parameters

This step has 1 tuning parameters:

• neighbors: # Nearest Neighbors (type: integer, default: 5)

Case weights

The underlying operation does not allow for case weights.

References

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

See Also

Other imputation steps: step_impute_bag(), step_impute_linear(), step_impute_lower(), step_impute_mean(), step_impute_median(), step_impute_mode(), step_impute_roll()

Examples

library(recipes)
data(biomass, package = "modeldata")

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_impute_knn(all_predictors(), neighbors = 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)