imputeCellReg: Cellwise-robust regression imputation for mixed data

In VIM: Visualization and Imputation of Missing Values

Cellwise-robust regression imputation for mixed data

Description

IRMI-style imputation using cellwise-robust regression as the inner engine. Three engines are available: CRM (Filzmoser et al. 2020), Shooting S (Öllerer et al. 2016), and a cellwise-weighted MM hybrid.

Usage

imputeCellReg(
  data,
  engine = "crm",
  maxit = 50,
  eps = 0.005,
  uncert = "pmm",
  trace = FALSE
)

Arguments

data	data.frame with missing values (mixed continuous + categorical)
engine	regression engine: "crm" (default), "cellwise-mm", or "shooting-s"
maxit	maximum outer IRMI iterations (default: 50)
eps	convergence tolerance (default: 5e-3)
uncert	imputation uncertainty: "pmm" (default), "normalerror", or "none"
trace	logical; print progress

Details

The function cycles through all variables with missing values (IRMI framework), fitting a cellwise-robust regression of each variable on all others. The engine argument selects the regression method:

"crm"

CRM (Cellwise Robust M-regression) from the crmReg package. Uses SPADIMO for cellwise outlier detection within each regression. Requires crmReg.

"cellwise-mm"

Hybrid: compute cell weights via MCD conditional residuals, then fit MM-estimation (lmrob) with row weights derived from cell weights. The MM-estimator provides high breakdown point.

"shooting-s"

Shooting S-estimator (Öllerer et al. 2016). Iterates between cellwise detection and S-estimation. Implemented from the published algorithm.

Categorical variables are imputed via weighted multinomial logistic regression, with row weights derived from the continuous cell weights.

Value

A list with components:

data_imputed	the imputed data.frame
cellweights	n x p matrix of cell weights (1 = clean)
converged	logical
iterations	number of outer iterations

Author(s)

Matthias Templ

References

P. Filzmoser, S. Höppner, I. Ortner, S. Serneels, S. Van Aelst (2020) Cellwise robust M regression. Computational Statistics and Data Analysis, 147, 106944.

V. Öllerer, A. Alfons, C. Croux (2016) The shooting S-estimator for robust regression. Computational Statistics, 31(3), 829–844.

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