step_modeimpute: Impute Nominal Data Using the Most Common Value

Description

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

Usage

step_modeimpute(recipe, ..., role = NA, trained = FALSE, modes = NULL)

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.

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.

Value

An updated version of recipe with the new step added to the sequence of existing steps (if any).

Details

step_modeimpute 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.

Examples

Run this code

# NOT RUN {
data("credit_data")

## missing data per column
vapply(credit_data, function(x) mean(is.na(x)), c(num = 0))

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_modeimpute(Status, Home, Marital)

imp_models <- prep(impute_rec, training = credit_tr)

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

table(credit_te$Home, imputed_te$Home, useNA = "always")
# }