step_impute_mode creates a specification of a recipe step that will substitute missing values of nominal variables by the training set mode of those variables.

step_impute_mode(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  modes = NULL,
  ptype = NULL,
  skip = FALSE,
  id = rand_id("impute_mode")
)

step_modeimpute(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  modes = NULL,
  ptype = NULL,
  skip = FALSE,
  id = rand_id("impute_mode")
)

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 this step. See selections() for more details.

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.

modes

A named character vector of modes. This is NULL until computed by prep.recipe().

ptype

A data frame prototype to cast new data sets to. This is commonly a 0-row slice of the training set.

skip

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.

id

A character string that is unique to this step to identify it.

Value

An updated version of recipe with the new step added to the sequence of any existing operations.

Details

step_impute_mode estimates the variable modes from the data used in the training argument of prep.recipe. bake.recipe then applies the new values to new data sets using these values. If the training set data has more than one mode, one is selected at random.

When you tidy() this step, a tibble with columns terms (the selectors or variables selected) and model (the mode value) is returned.

As of recipes 0.1.16, this function name changed from step_modeimpute() to step_impute_mode().

See also

Examples

library(modeldata)
data("credit_data")

## missing data per column
vapply(credit_data, function(x) mean(is.na(x)), c(num = 0))
#>       Status    Seniority         Home         Time          Age      Marital 
#> 0.0000000000 0.0000000000 0.0013471037 0.0000000000 0.0000000000 0.0002245173 
#>      Records          Job     Expenses       Income       Assets         Debt 
#> 0.0000000000 0.0004490346 0.0000000000 0.0855410867 0.0105523125 0.0040413112 
#>       Amount        Price 
#> 0.0000000000 0.0000000000 

set.seed(342)
in_training <- sample(1:nrow(credit_data), 2000)

credit_tr <- credit_data[ in_training, ]
credit_te <- credit_data[-in_training, ]
missing_examples <- c(14, 394, 565)

rec <- recipe(Price ~ ., data = credit_tr)

impute_rec <- rec %>%
  step_impute_mode(Status, Home, Marital)

imp_models <- prep(impute_rec, training = credit_tr)

imputed_te <- bake(imp_models, new_data = credit_te, everything())

table(credit_te$Home, imputed_te$Home, useNA = "always")
#>          
#>           ignore other owner parents priv rent <NA>
#>   ignore      13     0     0       0    0    0    0
#>   other        0   176     0       0    0    0    0
#>   owner        0     0  1171       0    0    0    0
#>   parents      0     0     0     436    0    0    0
#>   priv         0     0     0       0  135    0    0
#>   rent         0     0     0       0    0  519    0
#>   <NA>         0     0     4       0    0    0    0

tidy(impute_rec, number = 1)
#> # A tibble: 3 × 3
#>   terms   model id               
#>   <chr>   <chr> <chr>            
#> 1 Status  NA    impute_mode_Hlm2y
#> 2 Home    NA    impute_mode_Hlm2y
#> 3 Marital NA    impute_mode_Hlm2y
tidy(imp_models, number = 1)
#> # A tibble: 3 × 3
#>   terms   model   id               
#>   <chr>   <chr>   <chr>            
#> 1 Status  good    impute_mode_Hlm2y
#> 2 Home    owner   impute_mode_Hlm2y
#> 3 Marital married impute_mode_Hlm2y