imputeCellMM: Cell-weighted MM imputation for mixed data (Path A)

In VIM: Visualization and Imputation of Missing Values

Cell-weighted MM imputation for mixed data (Path A)

Description

Uses a cell-weighted MM-estimator for each variable regression: S-step on unweighted data (high breakdown), M-step with cell weights (fine-grained downweighting). This preserves the S-estimator's breakdown guarantee while incorporating cellwise information.

Usage

imputeCellMM(
  data,
  maxit = 50,
  eps = 0.005,
  alpha_weight = 4.685,
  init_weights = "mcd",
  uncert = "pmm",
  trace = FALSE
)

Arguments

data	data.frame with missing values
maxit	maximum outer IRMI iterations (default: 50)
eps	convergence tolerance (default: 5e-3)
alpha_weight	tuning constant for bisquare cell weights (default: 4.685)
init_weights	initial cell weight method: "mcd" or "univariate" (default: "mcd")
uncert	uncertainty method: "pmm", "normalerror", or "none"
trace	logical

Value

list with data_imputed, cellweights, converged, iterations

Author(s)

Matthias Templ

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