step_bagimpute {recipes} | R Documentation |
step_bagimpute
creates a specification of a
recipe step that will create bagged tree models to impute
missing data.
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(...) ## S3 method for class 'step_bagimpute' tidy(x, ...)
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 |
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 |
options |
A list of options to
|
impute_with |
A call to |
seed_val |
A integer used to create reproducible models. The same seed is used across all imputation models. |
x |
A |
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.
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).
Kuhn, M. and Johnson, K. (2013). Applied Predictive Modeling. Springer Verlag.
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)] tidy(impute_rec, number = 1) tidy(imp_models, number = 1)