Package:
mixgb
Authors:
Yongshi Deng [aut, cre], Thomas Lumley [ths]
Category:
Single Imputation, Analysis for Incomplete Data
Use-Cases:
Multiple imputation for large datasets.
Popularity:
Description:
Multiple imputation using ‘XGBoost’, subsampling, and predictive mean matching as described in Deng and Lumley (2023) doi:10.1080/10618600.2023.2252501. The package supports various types of variables, offers flexible settings, and enables saving an imputation model to impute new data. Data processing and memory usage have been optimised to speed up the imputation process.
Algorithms for Imputation:
impute_new(): Impute new data with a saved mixgb imputer objectmixgb(): This function is used to generate multiply-imputed datasets using XGBoost, subsampling and predictive mean matching (PMM),mixgb_cv(): Use cross-validation to find the optimal nrounds for an Mixgb imputer. Note that this method relies on the complete cases of a dataset to obtain the optimal nrounds.
Algorithms for Amputation:
createNA()- This function creates missing values under the missing complete at random (MCAR) mechanism. It is for demonstration purposes only.
Datasets:
nhanes3- A small subset of the NHANES III (1988-1994) newborn data,nhanes3_newborn- NHANESIII (1988-1994) newborn data.
Further Information:
Package repository: https://github.com/agnesdeng/mixgb
Documentation: https://www.rdocumentation.org/packages/mixgb/versions/1.5.2/topics/mixgb
Yongshi Deng, Thomas Lumley, “Multiple Imputation Through XGBoost”, https://doi.org/10.1080/10618600.2023.2252501
Input:
data.frame