Author: Yongshi Deng
Date: 2026-01-17

# Imputing newdata with a saved mixgb imputer

#### Yongshi Deng

#### 2026-01-17

## Impute new unseen data using a saved imputer object

To demonstrate how to impute new data using a saved imputer, we first split the `newborn` dataset into training data and test data.

    set.seed(2026)
    n <- nrow(newborn)
    idx <- sample(1:n, size = round(0.7 * n), replace = FALSE)
    train_data <- newborn[idx, ]
    test_data <- newborn[-idx, ]

Next, we impute the training data using `mixgb()`. This allows us to generate `m` imputed datasets and save the corresponding imputation models by setting `save.models = TRUE`. By default, models for all variables with missing values in the training data are saved (`save.vars = NULL`).

If the new or unseen data may contain missing values in additional variables, users can save models for all variables by setting `save.vars = colnames(train_data)`. This can be time-consuming, as a model needs to be trained and saved for each variable. When users are confident that only certain variables will have missing values in future data, it is more efficient to specify only those variables in `save.vars`.

To save the imputer object, specify a local directory using the `save.models.folder` argument in `mixgb()`. In the backend, models are saved in JSON format via `xgb.save()`. This approach, recommended by XGBoost, is preferred over directly saving the XGBoost models with `saveRDS()` because it ensures compatibility with future XGBoost releases.

    # create a temporary folder
    tmp_folder <- file.path(tempdir(), "mixgb_models")
    dir.create(tmp_folder, showWarnings = FALSE)

    # obtain m imputed datasets for train_data and save imputation models
    mixgb_obj <- mixgb(
      data = train_data, m = 5,
      save.models = TRUE, save.models.folder = tmp_folder
    )

    saveRDS(
      object = mixgb_obj,
      file = file.path(tmp_folder, "mixgb_imputer.rds")
    )

If users specify the `save.models.folder`, the return object will include the following:

- `imputed_data`: a list of `m` imputed datasets for training data

- `XGB.models`: a list of directories of `m` sets of XGBoost models for variables specified in `save.vars`.

- `params`: a list of parameters that are required for imputing new data using `impute_new()` later on.

- `XGB.save` : a parameter indicates whether `XGB.models` are the saved models or the directories for the saved models.

As the `mixgb_obj` does not contain the models themselves, users need not worry about saving this object via `saveRDS()`. For later use, one can load the object into R and impute new data.

To impute new data with this saved imputer object, we can use the `impute_new()` function.

    mixgb_obj <- readRDS(file = file.path(tmp_folder, "mixgb_imputer.rds"))
    test_imp <- impute_new(object = mixgb_obj, newdata = test_data)

Users can decide whether to perform initial imputation on new data. By default, the training data is used to impute missing values in the new dataset initially (`initial.newdata = FALSE`). After this step, the missing values in the new data are imputed using the saved models from the imputer object. This approach is considerably faster, as it avoids retraining the imputation models.

    test_imp <- impute_new(object = mixgb_obj, newdata = test_data)

When predictive mean matching (PMM) is used in `mixgb()`, predicted values of missing entries in the new dataset will be matched with donors from the training data. Users can also specify the number of donors for PMM. The default setting `pmm.k = NULL` indicates that the `pmm.k` value from the training object will be inherited.

Similarly, the number of imputed datasets for the new data can be set via `m` in `impute_new()`. This value must be less than or equal to the `m` used in `mixgb()`. If not specified, `impute_new()` will use the `m` value stored in the saved imputer object.

    test_imp <- impute_new(
      object = mixgb_obj, newdata = test_data,
      initial.newdata = FALSE, pmm.k = 3, m = 4
    )