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

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

# S3 method for step_impute_linear
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 variables. For step_impute_linear, this indicates the variables to be imputed; these variables must be of type numeric. When used with imp_vars, the dots indicates which variables are used to predict the missing data in each variable. 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.

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.recipe().

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

id

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

x

A step_impute_linear 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 bagged tree object).

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.

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)
#> #> Attaching package: ‘ggplot2’
#> The following object is masked from ‘package:kernlab’: #> #> alpha
ggplot(imputed, aes(x = original, y = imputed)) + geom_abline(col = "green") + geom_point(alpha = .3) + coord_equal() + labs(title = "Imputed Values")