step_impute_bag {recipes} | R Documentation |
Impute 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")
)
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 |
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 |
trees |
An integer for the number of bagged trees to use in each model. |
models |
The |
options |
A list of options to |
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 |
id |
A character string that is unique to this step to identify it. |
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 any existing operations.
Tidying
When you tidy()
this step, a tibble with columns
terms
(the selectors or variables selected) and model
(the bagged tree object) is returned.
When you tidy()
this step, a tibble is returned with
columns terms
, model
, and id
:
- terms
character, the selectors or variables selected
- model
list, the bagged tree object
- id
character, id of this step
Tuning Parameters
This step has 1 tuning parameters:
-
trees
: # Trees (type: integer, default: 25)
Case weights
The underlying operation does not allow for case weights.
References
Kuhn, M. and Johnson, K. (2013). Applied Predictive Modeling. Springer Verlag.
See Also
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))
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)
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)
credit_te[missing_examples, ]
imputed_te[missing_examples, names(credit_te)]
tidy(impute_rec, number = 1)
tidy(imp_models, number = 1)
## End(Not run)