Imputes missing values in a numeric matrix by iteratively training per-column XGBoost models on observed entries and predicting missing ones. Columns that are entirely missing are removed. Initial imputation uses column means.
Usage
xgboost_imputation(
data,
maxiter = 10L,
max_depth = 2L,
nrounds = 50L,
decreasing = FALSE,
num_workers = 1L,
verbose = TRUE
)Arguments
- data
Numeric matrix with missing values (NA).
- maxiter
Maximum number of imputation iterations (default 10).
- max_depth
Maximum tree depth for XGBoost (default 2).
- nrounds
Number of boosting rounds per variable (default 50).
- decreasing
Logical. If TRUE, impute variables with most missing values first. Default FALSE (fewest missing first).
- num_workers
Number of parallel workers for BiocParallel. Default 1 (sequential).
- verbose
Logical, print progress (default TRUE).