step_meanimpute: Impute Numeric Data Using the Mean

Description

step_meanimpute creates a specification of a recipe step that will substitute missing values of numeric variables by the training set mean of those variables.

Usage

step_meanimpute(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  means = NULL,
  trim = 0,
  skip = FALSE,
  id = rand_id("meanimpute")
)
# S3 method for step_meanimpute
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.

means

A named numeric vector of means. This is NULL until computed by prep.recipe(). Note that, if the original data are integers, the mean will be converted to an integer to maintain the same a data type.

trim

The fraction (0 to 0.5) of observations to be trimmed from each end of the variables before the mean is computed. Values of trim outside that range are taken as the nearest endpoint.

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

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

A step_meanimpute 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 mean value).

Details

step_meanimpute estimates the variable means from the data used in the training argument of prep.recipe. bake.recipe then applies the new values to new data sets using these averages.

Examples

Run this code

# NOT RUN {
library(modeldata)
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_meanimpute(Income, Assets, Debt)

imp_models <- prep(impute_rec, training = credit_tr)

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

credit_te[missing_examples,]
imputed_te[missing_examples, names(credit_te)]

tidy(impute_rec, number = 1)
tidy(imp_models, number = 1)
# }