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 = 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.
Bare names or selectors functions that 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 nthread and eps.
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().
A character string of the selected variable names. This field
is a placeholder and will be populated once prep() is used.
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.
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 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().
When you tidy() this step, a tibble is returned with
columns terms, predictors, neighbors , and id:
character, the selectors or variables selected
character, selected predictors used to impute
integer, number of neighbors
character, id of this step
This step has 1 tuning parameters:
neighbors: # Nearest Neighbors (type: integer, default: 5)
The underlying operation does not allow for case weights.
Gower, C. (1971) "A general coefficient of similarity and some of its properties," Biometrics, 857-871.
Other imputation steps:
step_impute_bag(),
step_impute_linear(),
step_impute_lower(),
step_impute_mean(),
step_impute_median(),
step_impute_mode(),
step_impute_roll()
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