step_impute_mean: Impute numeric data using the mean

In recipes: Preprocessing and Feature Engineering Steps for Modeling

Impute numeric data using the mean

Description

step_impute_mean() 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_impute_mean(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  means = NULL,
  trim = 0,
  skip = FALSE,
  id = rand_id("impute_mean")
)

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.
means	A named numeric vector of means. This is NULL until computed by prep(). Note that, if the original data are integers, the mean will be converted to an integer to maintain the same 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()? While all operations are baked when prep() 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.

Details

step_impute_mean 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.

As of recipes 0.1.16, this function name changed from step_meanimpute() to step_impute_mean().

Value

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

Tidying

When you tidy() this step, a tibble is returned with columns terms, value , and id:

terms

character, the selectors or variables selected

value

numeric, the mean value

id

character, id of this step

Tuning Parameters

This step has 1 tuning parameters:

• trim: Amount of Trimming (type: double, default: 0)

Case weights

This step performs an unsupervised operation that can utilize case weights. As a result, case weights are only used with frequency weights. For more information, see the documentation in case_weights and the examples on tidymodels.org.

See Also

Other imputation steps: step_impute_bag(), step_impute_knn(), step_impute_linear(), step_impute_lower(), step_impute_median(), step_impute_mode(), step_impute_roll()

Examples

data("credit_data", package = "modeldata")

## 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_impute_mean(Income, Assets, Debt)

imp_models <- prep(impute_rec, training = credit_tr)

imputed_te <- bake(imp_models, new_data = credit_te)

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

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

recipes documentation built on July 4, 2024, 9:06 a.m.