Impute via k-nearest neighborsSource:
step_impute_knn() creates a specification of a recipe step that will
impute missing data using nearest neighbors.
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") ) step_knnimpute( 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") )
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.
Not used by this step since no new variables are created.
A logical to indicate if the quantities for preprocessing have been estimated.
The number of neighbors.
A call to
imp_varsto 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.
A named list of options to pass to
gower::gower_topn(). Available options are currently
A tibble of data that will reflect the data preprocessing done up to the point of this imputation step. This is
NULLuntil the step is trained by
A character string of the selected variable names. This field is a placeholder and will be populated once
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 = TRUEas it may affect the computations for subsequent operations.
A character string that is unique to this step to identify it.
An updated version of
recipe with the new step added to the
sequence of any existing operations.
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
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.
recipes 0.1.16, this function name changed from
tidy() this step, a tibble with columns
terms (the selectors or variables for imputation),
(those variables used to impute), and
neighbors is returned.
This step has 1 tuning parameters:
neighbors: # Nearest Neighbors (type: integer, default: 5)
Gower, C. (1971) "A general coefficient of similarity and some of its properties," Biometrics, 857-871.
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$carbon) #> Min. 1st Qu. Median Mean 3rd Qu. Max. #> 27.80 44.24 47.30 47.96 49.00 79.34 cbind( before = biomass_te_whole$carbon[carb_missing], after = imputed$carbon[carb_missing] ) #> before after #> [1,] 46.83 47.43000 #> [2,] 47.80 47.53333 #> [3,] 46.40 46.21000 summary(biomass_te_whole$nitrogen) #> Min. 1st Qu. Median Mean 3rd Qu. Max. #> 0.010 0.295 0.690 1.092 1.450 4.790 cbind( before = biomass_te_whole$nitrogen[nitro_missing], after = imputed$nitrogen[nitro_missing] ) #> before after #> [1,] 1.24 0.59333333 #> [2,] 0.30 0.92333333 #> [3,] 0.06 0.04666667 tidy(ratio_recipe, number = 1) #> # A tibble: 1 × 4 #> terms predictors neighbors id #> <chr> <chr> <dbl> <chr> #> 1 all_predictors() NA 3 impute_knn_iyPXM tidy(ratio_recipe2, number = 1) #> # A tibble: 20 × 4 #> terms predictors neighbors id #> <chr> <chr> <dbl> <chr> #> 1 carbon hydrogen 3 impute_knn_iyPXM #> 2 carbon oxygen 3 impute_knn_iyPXM #> 3 carbon nitrogen 3 impute_knn_iyPXM #> 4 carbon sulfur 3 impute_knn_iyPXM #> 5 hydrogen carbon 3 impute_knn_iyPXM #> 6 hydrogen oxygen 3 impute_knn_iyPXM #> 7 hydrogen nitrogen 3 impute_knn_iyPXM #> 8 hydrogen sulfur 3 impute_knn_iyPXM #> 9 oxygen carbon 3 impute_knn_iyPXM #> 10 oxygen hydrogen 3 impute_knn_iyPXM #> 11 oxygen nitrogen 3 impute_knn_iyPXM #> 12 oxygen sulfur 3 impute_knn_iyPXM #> 13 nitrogen carbon 3 impute_knn_iyPXM #> 14 nitrogen hydrogen 3 impute_knn_iyPXM #> 15 nitrogen oxygen 3 impute_knn_iyPXM #> 16 nitrogen sulfur 3 impute_knn_iyPXM #> 17 sulfur carbon 3 impute_knn_iyPXM #> 18 sulfur hydrogen 3 impute_knn_iyPXM #> 19 sulfur oxygen 3 impute_knn_iyPXM #> 20 sulfur nitrogen 3 impute_knn_iyPXM