recipes: step_impute_bag – R documentation

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step_impute_bag

Imputation via Bagged Trees

Description

step_impute_bag creates a specification of a recipe step that will create bagged tree models to impute missing data.

Usage

step_impute_bag(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  impute_with = imp_vars(all_predictors()),
  trees = 25,
  models = NULL,
  options = list(keepX = FALSE),
  seed_val = sample.int(10^4, 1),
  skip = FALSE,
  id = rand_id("impute_bag")
)

step_bagimpute(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  impute_with = imp_vars(all_predictors()),
  trees = 25,
  models = NULL,
  options = list(keepX = FALSE),
  seed_val = sample.int(10^4, 1),
  skip = FALSE,
  id = rand_id("impute_bag")
)

imp_vars(...)

## S3 method for class 'step_impute_bag'
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_bag`, this indicates the variables to be imputed. When used with `imp_vars`, the dots indicate 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.
`trees`	An integer for the number of bagged trees to use in each model.
`models`	The `ipred::ipredbagg()` objects are stored here once this bagged trees have be trained by `prep.recipe()`.
`options`	A list of options to `ipred::ipredbagg()`. Defaults are set for the arguments `nbagg` and `keepX` but others can be passed in. Note that the arguments `X` and `y` should not be passed here.
`seed_val`	An integer used to create reproducible models. The same seed is used across all imputation models.
`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_bag` object.

Details

For each variable requiring imputation, a bagged tree is created where the outcome is the variable of interest and the predictors are any other variables listed in the impute_with formula. One advantage to the bagged tree is that is can accept predictors that have missing values themselves. This imputation method can be used when the variable of interest (and predictors) are numeric or categorical. Imputed categorical variables will remain categorical. Also, integers will be imputed to integer too.

Note that if a variable that is to be imputed is also in impute_with, this variable will be ignored.

It is possible that missing values will still occur after imputation if a large majority (or all) of the imputing variables are also missing.

As of recipes 0.1.16, this function name changed from step_bagimpute() to step_impute_bag().

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).

References

Kuhn, M. and Johnson, K. (2013). Applied Predictive Modeling. Springer Verlag.

Examples

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)
## Not run: 
impute_rec <- rec %>%
  step_impute_bag(Status, Home, Marital, Job, 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)

## Specifying which variables to imputate with

 impute_rec <- rec %>%
  step_impute_bag(Status, Home, Marital, Job, Income, Assets, Debt,
                 impute_with = imp_vars(Time, Age, Expenses),
                 # for quick execution, nbagg lowered
                 options = list(nbagg = 5, keepX = FALSE))

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)

## End(Not run)

recipes

Preprocessing Tools to Create Design Matrices

v0.1.16

MIT + file LICENSE

Authors

Max Kuhn [aut, cre], Hadley Wickham [aut], RStudio [cph]

Initial release