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,
  skip = FALSE,
  id = rand_id("impute_mode")
)

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

# S3 method for step_impute_mode
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 which variables are affected by the step. See selections() for more details. For the tidy method, these are not currently used.

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().

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.

x

A step_impute_mode 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 selected) and model (the mode value).

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.

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

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 x 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 x 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