R: Impute Numeric Data Using the Mean

step_meanimpute {recipes} | R Documentation

## 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)

    ## S3 method for class '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()`.
`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
`x` | A `step_meanimpute` object.

### 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.

### 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).

### Examples

    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, newdata = credit_te, everything())

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

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

[Package recipes version 0.1.2 Index]