Hybrid Imputation: Random Forest + LCMD

This function performs hybrid imputation on selected columns of a data frame by combining Random Forest (RF) imputation (via missForest) and left-censored missing data (LCMD) imputation (via imputeLCMD). It selects the method per feature based on the out-of-bag error (OOBE) from the RF model.

Usage

hybrid_imputation(
  df,
  target_cols = NULL,
  is_qc = NULL,
  method = c("RF-LCMD"),
  oobe_threshold = 0.1,
  control_RF = list(),
  control_LCMD = list()
)

Arguments

df

A data frame with samples (rows) and features (columns).

target_cols

A character vector of column names or a single regular expression to identify target columns for imputation. If NULL, all columns are considered.

is_qc

A logical vector indicating which rows are QC samples. Must match nrow(df).

method

Imputation strategy to use (currently only "RF-LCMD" supported).

oobe_threshold

Numeric. Features with OOBE below this threshold will use RF, others will use LCMD.

control_RF

A named list of control arguments for missForest::missForest(). Also supports n_cores (internal).

control_LCMD

A named list of control arguments for imputeLCMD::impute.MAR.MNAR(), including mode = "overall" or "column-wise".

Value

A named list with the following components:

hybrid_rf_lcmd

The fully imputed data frame combining RF and LCMD decisions.

rf

The RF-imputed data frame (non-QC rows only, in full column structure).

lcmd

The LCMD-imputed data frame (non-QC rows only, in full column structure).

oob

A named numeric vector of feature-level OOB errors from RF.

See also

run_rf_imputation, run_lcmd_imputation