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(...)

## 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. 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.

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().

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()? 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.

## Value

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

## 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().

## Tidying

When you tidy() this step, a tibble with columns terms (the selectors or variables selected) and model (the bagged tree object) is returned.

## Case weights

The underlying operation does not allow for case weights.

## References

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

Other imputation steps: step_impute_knn(), step_impute_linear(), step_impute_lower(), step_impute_mean(), 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))
#>       Status    Seniority         Home         Time          Age
#> 0.0000000000 0.0000000000 0.0013471037 0.0000000000 0.0000000000
#>      Marital      Records          Job     Expenses       Income
#> 0.0002245173 0.0000000000 0.0004490346 0.0000000000 0.0855410867
#>       Assets         Debt       Amount        Price
#> 0.0105523125 0.0040413112 0.0000000000 0.0000000000

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)
if (FALSE) {
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)
}