Imputation via K-Nearest Neighbors
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")
)
## S3 method for class 'step_impute_knn'
tidy(x, ...)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. |
neighbors |
The number of neighbors. |
impute_with |
A call to |
options |
A named list of options to pass 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 |
skip |
A logical. Should the step be skipped when the
recipe is baked by |
id |
A character string that is unique to this step to identify it. |
x |
A |
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().
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 neighbors.
Gower, C. (1971) "A general coefficient of similarity and some of its properties," Biometrics, 857-871.
library(recipes)
library(modeldata)
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_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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.