step_knnimpute creates a specification of a recipe step that will impute missing data using nearest neighbors.

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("knnimpute")
)

# S3 method for step_knnimpute
tidy(x, ...)

## 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. For step_knnimpute, this indicates the variables to be imputed. When used with imp_vars, the dots indicate which variables are used to predict the missing data in each variable. See selections() for more details. For the tidy method, these are not currently used. 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_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. 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.recipe(). The column names that will be imputed and used for imputation. This is NULL until the step is trained by prep.recipe(). A logical. Should the step be skipped when the recipe is baked by bake.recipe()? While all operations are baked when prep.recipe() 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. A step_knnimpute object.

## Value

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.

## 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.

## References

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

## Examples

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_knnimpute(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 x 4
#>   terms            predictors neighbors id
#>   <chr>            <chr>          <dbl> <chr>
#> 1 all_predictors() NA                 3 knnimpute_iyPXMtidy(ratio_recipe2, number = 1)
#> # A tibble: 20 x 4
#>    terms    predictors neighbors id
#>    <chr>    <chr>          <dbl> <chr>
#>  1 carbon   hydrogen           3 knnimpute_iyPXM
#>  2 carbon   oxygen             3 knnimpute_iyPXM
#>  3 carbon   nitrogen           3 knnimpute_iyPXM
#>  4 carbon   sulfur             3 knnimpute_iyPXM
#>  5 hydrogen carbon             3 knnimpute_iyPXM
#>  6 hydrogen oxygen             3 knnimpute_iyPXM
#>  7 hydrogen nitrogen           3 knnimpute_iyPXM
#>  8 hydrogen sulfur             3 knnimpute_iyPXM
#>  9 oxygen   carbon             3 knnimpute_iyPXM
#> 10 oxygen   hydrogen           3 knnimpute_iyPXM
#> 11 oxygen   nitrogen           3 knnimpute_iyPXM
#> 12 oxygen   sulfur             3 knnimpute_iyPXM
#> 13 nitrogen carbon             3 knnimpute_iyPXM
#> 14 nitrogen hydrogen           3 knnimpute_iyPXM
#> 15 nitrogen oxygen             3 knnimpute_iyPXM
#> 16 nitrogen sulfur             3 knnimpute_iyPXM
#> 17 sulfur   carbon             3 knnimpute_iyPXM
#> 18 sulfur   hydrogen           3 knnimpute_iyPXM
#> 19 sulfur   oxygen             3 knnimpute_iyPXM
#> 20 sulfur   nitrogen           3 knnimpute_iyPXM