Skip to contents

Multiple imputation through XGBoost

Source: R/mixgb.R
mixgb.Rd

This function is used to generate multiply-imputed datasets using XGBoost, subsampling and predictive mean matching (PMM).

Usage

mixgb(
  data,
  m = 5,
  maxit = 1,
  ordinalAsInteger = FALSE,
  pmm.type = NULL,
  pmm.k = 5,
  pmm.link = "prob",
  initial.num = "normal",
  initial.int = "mode",
  initial.fac = "mode",
  save.models = FALSE,
  save.vars = NULL,
  save.models.folder = NULL,
  verbose = F,
  xgb.params = list(),
  nrounds = 100,
  early_stopping_rounds = NULL,
  print_every_n = 10L,
  xgboost_verbose = 0,
  ...
)

Arguments

data

A data.frame or data.table with missing values

m

The number of imputed datasets. Default: 5

maxit

The number of imputation iterations. Default: 1

ordinalAsInteger

Whether to convert ordinal factors to integers. By default, ordinalAsInteger = FALSE. Setting ordinalAsInteger = TRUE may speed up the imputation process for large datasets.

pmm.type

The type of predictive mean matching (PMM). Possible values:

  • NULL (default): Imputations without PMM;

  • 0: Imputations with PMM type 0;

  • 1: Imputations with PMM type 1;

  • 2: Imputations with PMM type 2;

  • "auto": Imputations with PMM type 2 for numeric/integer variables; imputations without PMM for categorical variables.

pmm.k

The number of donors for predictive mean matching. Default: 5

The link for predictive mean matching in binary variables

  • "prob" (default): use probabilities;

  • "logit": use logit values.

initial.num

Initial imputation method for numeric type data:

  • "normal" (default);

  • "mean";

  • "median";

  • "mode";

  • "sample".

initial.int

Initial imputation method for integer type data:

  • "mode" (default);

  • "sample".

initial.fac

Initial imputation method for factor type data:

  • "mode" (default);

  • "sample".

save.models

Whether to save imputation models for imputing new data later on. Default: FALSE

save.vars

For the purpose of imputing new data, the imputation models for response variables specified in save.vars will be saved. The values in save.vars can be a vector of names or indices. By default, only the imputation models for variables with missing values in the original data will be saved (save.vars = NULL). To save imputation models for all variables, users can specify save.vars = colnames(data).

save.models.folder

Users can specify a directory to save all imputation models. Models will be saved in JSON format by internally calling xgb.save(), which is recommended by XGBoost.

verbose

Verbose setting for mixgb. If TRUE, will print out the progress of imputation. Default: FALSE.

xgb.params

A list of XGBoost parameters. For more details, please check XGBoost documentation on parameters.

nrounds

The maximum number of boosting iterations for XGBoost. Default: 100

early_stopping_rounds

An integer value k. XGBoost training will stop if the validation performance has not improved for k rounds. Default: 10.

print_every_n

Print XGBoost evaluation information at every nth iteration if xgboost_verbose > 0.

xgboost_verbose

Verbose setting for XGBoost training: 0 (silent), 1 (print information) and 2 (print additional information). Default: 0

...

Extra arguments to be passed to XGBoost

Value

If save.models = FALSE, this function will return a list of m imputed datasets. If save.models = TRUE, it will return an object with imputed datasets, saved models and parameters.

Examples

# obtain m multiply datasets without saving models
params <- list(max_depth = 3, subsample = 0.7, nthread = 2)
mixgb.data <- mixgb(data = nhanes3, m = 2, xgb.params = params, nrounds = 10)

# obtain m multiply imputed datasets and save models for imputing new data later on
mixgb.obj <- mixgb(data = nhanes3, m = 2, xgb.params = params, nrounds = 10,
                   save.models = TRUE, save.models.folder = tempdir())