step_knnimpute {recipes} | R Documentation |
step_knnimpute
creates a specification of a recipe step that
will impute missing data using nearest neighbors.
step_knnimpute(recipe, ..., role = NA, trained = FALSE, K = 5, impute_with = imp_vars(all_predictors()), ref_data = NULL, columns = NULL)
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
|
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 |
ref_data |
A tibble of data that will reflect the data preprocessing
done up to the point of this imputation step. This is
|
columns |
The column names that will be imputed and used for
imputation. This is |
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.
An updated version of recipe
with the
new step added to the sequence of existing steps (if any).
Gower, C. (1971) "A general coefficient of similarity and some of its properties," Biometrics, 857-871.
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])