recipes (version 0.1.0)

step_bagimpute: Imputation via Bagged Trees

Description

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

Usage

step_bagimpute(recipe, ..., role = NA, trained = FALSE, models = NULL,
  options = list(nbagg = 25, keepX = FALSE),
  impute_with = imp_vars(all_predictors()), seed_val = sample.int(10^4, 1))
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. For step_bagimpute, this indicates the variables to be imputed. 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.

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.

models

The ipredbagg objects are stored here once this bagged trees have be trained by prep.recipe.

options

A list of options to 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.

impute_with

A call to imp_vars to specify which variables are used to impute the variables that can inlcude specific variable names seperated 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.

seed_val

A integer used to create reproducible models. The same seed is used across all imputation models.

Value

An updated version of recipe with the new step added to the sequence of existing steps (if any).

Details

For each variables 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.

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.

References

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

Examples

# NOT RUN {
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_bagimpute(Status, Home, Marital, Job, 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)]
# }