regressionImp: Regression Imputation (via vimpute)

In VIM: Visualization and Imputation of Missing Values

Regression Imputation (via vimpute)

Description

Impute missing values based on a regression / classification model.

Usage

regressionImp(
  formula,
  data,
  family = "AUTO",
  robust = FALSE,
  imp_var = TRUE,
  imp_suffix = "imp",
  mod_cat = FALSE
)

Arguments

formula	model formula to impute one variable
data	A data.frame containing the data
family	ignored in this wrapper version (only "AUTO"-like behaviour via vimpute)
robust	logical; if TRUE use method = "robust", otherwise "regularized"
imp_var	logical; if TRUE create TRUE/FALSE variables indicating imputation status
imp_suffix	suffix used for TF imputation variables
mod_cat	ignored in this wrapper version (classification handled by vimpute)

Details

By default (lm() for numeric responses and glm() for binary responses, with any number of predictors) the imputation is deterministic and matches a plain regression fit. When the design is rank-deficient (collinear predictors or p \ge n) or the response is a multi-level factor, it falls back to the regularized (glmnet) path via vimpute() with method = "regularized". If robust = TRUE, vimpute() with method = "robust" (lmrob() / glmrob()) is used.

Value

the imputed data set (same class as data)

See Also

Other imputation methods: hotdeck(), impPCA(), imputeCellEM(), imputeCellIRMI(), imputeCellM(), imputeCellMCD(), imputeCellwise(), imputeRobust(), imputeRobustChain(), irmi(), kNN(), matchImpute(), medianSamp(), rangerImpute(), sampleCat(), vimmi, vimpute(), xgboostImpute()

VIM documentation built on Sept. 2, 2026, 5:07 p.m.