# Imputing newdata with a saved mixgb imputer

#### Yongshi Deng

#### 2025-12-08

## Impute new unseen data using a saved imputer object

To demonstrate how to impute new data using a saved imputer, we first split the `nhanes3_newborn` dataset into training data and test data.

    set.seed(2022)
    n <- nrow(nhanes3)
    idx <- sample(1:n, size = round(0.7 * n), replace = FALSE)
    train.data <- nhanes3[idx, ]
    test.data <- nhanes3[-idx, ]

Next we impute the training data using `mixgb()`. We can use the training data to generate `m` imputed datasets and save their imputation models. To achieve this, users need to set `save.models = TRUE`. By default, imputation models for all variables with missing values in the training data will be saved (`save.vars = NULL`). However, it is possible that unseen data may have missing values in other variables. To be thorough, users can save models for all variables by setting `save.vars = colnames(train.data)`. Note that this may take significantly longer as it requires training and saving a model for each variable. In cases where users are confident that only certain variables will have missing values in the new data, it is advisable to specify the names or indices of these variables in `save.vars` rather than saving models for all variables.

To save the imputer object, users need to specify a local directory in the parameter `save.models.folder` in the main function `mixgb()`. Models will be save as JSON format by calling `xgb.save()` internally. Saving XGBoost models in this way instead of using `saveRDS` in R is recommended by XGBoost. This can ensure that the imputation models can still be used in later release of XGBoost.

    # 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 = "C:/Users/.....")
    saveRDS(object = mixgb.obj, file = "C:/Users/.../mixgbimputer.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 = "C:/Users/.../mixgbimputer.rds")
    test.imputed <- impute_new(object = mixgb.obj, newdata = test.data)

Users can choose whether to use new data for initial imputation. By default, the information of training data is used to initially impute the missing data in the new dataset (`initial.newdata = FALSE`). After this, the missing values in the new dataset will be imputed using the saved models from the imputer object. This process will be considerably faster because it does not involve rebuilding the imputation models.

    test.imputed <- impute_new(object = mixgb.obj, newdata = test.data)

If PMM is used in `mixgb()`, predicted values of missing entries in the new dataset will be matched with donors from the training data. Additionally, users can set the number of donors to be used in PMM when imputing new data. The default setting `pmm.k = NULL` indicates that the same setting as the training object will be used.

Similarly, users can set the number of imputed datasets `m` in `impute_new()`. Note that this value has to be less than or equal to the `m` value specified in `mixgb()`. If this value is not specified, the function will use the same `m` value as the saved object.

    test.imputed <- impute_new(object = mixgb.obj, newdata = test.data, initial.newdata = FALSE, pmm.k = 3, m = 4)