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(...)
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. |
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).
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)]