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, 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("bagimpute") ) imp_vars(...) # S3 method for 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. |
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. |
x | A |
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).
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.
Kuhn, M. and Johnson, K. (2013). Applied Predictive Modeling. Springer Verlag.
library(modeldata) data("credit_data") ## missing data per column vapply(credit_data, function(x) mean(is.na(x)), c(num = 0))#> Status Seniority Home Time Age Marital #> 0.0000000000 0.0000000000 0.0013471037 0.0000000000 0.0000000000 0.0002245173 #> Records Job Expenses Income Assets Debt #> 0.0000000000 0.0004490346 0.0000000000 0.0855410867 0.0105523125 0.0040413112 #> Amount Price #> 0.0000000000 0.0000000000set.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_bagimpute(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_bagimpute(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) }