step_impute_linear: Impute numeric variables via a linear model
In recipes: Preprocessing and Feature Engineering Steps for Modeling

step_impute_linear

R Documentation

Impute numeric variables via a linear model

Description

step_impute_linear() creates a specification of a recipe step that will create linear regression models to impute missing data.

Usage

step_impute_linear(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  impute_with = imp_vars(all_predictors()),
  models = NULL,
  skip = FALSE,
  id = rand_id("impute_linear")
)

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 to be imputed; these variables must be of type `numeric`. When used with `imp_vars`, these dots indicate which variables are used to predict the missing data in each variable. 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.
`impute_with`	A call to `imp_vars` to specify which variables are used to impute the variables that can include specific variable names separated by commas or different selectors (see `selections()`). If a column is included in both lists to be imputed and to be an imputation predictor, it will be removed from the latter and not used to impute itself.
`models`	The `lm()` objects are stored here once the linear models have been trained by `prep()`.
`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

For each variable requiring imputation, a linear model is fit where the outcome is the variable of interest and the predictors are any other variables listed in the impute_with formula. Note that if a variable that is to be imputed is also in impute_with, this variable will be ignored.

The variable(s) to be imputed must be of type numeric. The imputed values will keep the same type as their original data (i.e, model predictions are coerced to integer as needed).

Since this is a linear regression, the imputation model only uses complete cases for the training set predictors.

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, model , and id:

terms: character, the selectors or variables selected
model: list, list of fitted lm() models
id: character, id of this step

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.

References

Kuhn, M. and Johnson, K. (2013). Feature Engineering and Selection https://bookdown.org/max/FES/handling-missing-data.html

Examples


data(ames, package = "modeldata")
set.seed(393)
ames_missing <- ames
ames_missing$Longitude[sample(1:nrow(ames), 200)] <- NA

imputed_ames <-
  recipe(Sale_Price ~ ., data = ames_missing) %>%
  step_impute_linear(
    Longitude,
    impute_with = imp_vars(Latitude, Neighborhood, MS_Zoning, Alley)
  ) %>%
  prep(ames_missing)

imputed <-
  bake(imputed_ames, new_data = ames_missing) %>%
  dplyr::rename(imputed = Longitude) %>%
  bind_cols(ames %>% dplyr::select(original = Longitude)) %>%
  bind_cols(ames_missing %>% dplyr::select(Longitude)) %>%
  dplyr::filter(is.na(Longitude))

library(ggplot2)
ggplot(imputed, aes(x = original, y = imputed)) +
  geom_abline(col = "green") +
  geom_point(alpha = .3) +
  coord_equal() +
  labs(title = "Imputed Values")

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