imputeCellIRMI: Cellwise-robust iterative regression imputation for mixed...

In VIM: Visualization and Imputation of Missing Values

Cellwise-robust iterative regression imputation for mixed data

Description

Extends IRMI (Templ, Kowarik, and Filzmoser, 2011) with cellwise contamination handling. Each conditional regression uses a cell-weighted IRWLS engine where per-cell weights in the design matrix downweight contaminated cells without discarding entire observations.

Usage

imputeCellIRMI(
  data,
  method = "tukey",
  alpha = NULL,
  maxit = 100,
  maxit_irwls = 50,
  eps = 0.005,
  eps_irwls = 1e-06,
  uncert = "pmm",
  weight_update = "multivariate",
  init_weights = "ddc",
  hard_threshold = 0.5,
  trace = FALSE
)

Arguments

data	a data.frame with missing values (mixed continuous and categorical variables are supported).
method	weight function: "tukey" (default, Tukey bisquare) or "huber" (Huber).
alpha	tuning constant. NULL (default) uses 1.345 for Huber and 4.685 for Tukey, giving 95% efficiency at the normal model.
maxit	maximum number of outer IRMI iterations (default: 100).
maxit_irwls	maximum number of inner IRWLS iterations per regression (default: 50).
eps	convergence tolerance for the outer loop (default: 5e-3). Convergence is declared when the relative change in imputed values falls below this threshold.
eps_irwls	convergence tolerance for the inner IRWLS (default: 1e-6).
uncert	imputation uncertainty method: "pmm" (predictive mean matching, default), "normalerror" (add normal noise), or "resid" (bootstrap residual).
weight_update	strategy for updating cell weights between outer iterations: "multivariate" (default) uses an MCD-based multivariate update for weight coherence across variables, or "univariate" updates each variable independently from its residuals.
init_weights	method for initialising cell weights, one of "ddc" (default; DetectDeviatingCells, requires the cellWise package and falls back to univariate weights when it is unavailable), "univariate" (per-column median/MAD standardisation), or "mcd" (minimum covariance determinant on the continuous block). The default is "ddc" because "mcd" downweights high-leverage points that carry the regression signal, which can make imputation worse than unconditional median imputation.
hard_threshold	numeric in [0, 1]. After convergence, cells with weight below this value are flagged as contaminated (default: 0.5).
trace	logical; if TRUE, print progress information.

Details

The algorithm works iteratively: in each outer iteration, every variable with missing values is used as response in a conditional regression on all remaining variables. For continuous responses, the custom cellIRWLS() engine fits a weighted regression where each cell in the design matrix receives its own weight reflecting potential cellwise contamination. For categorical responses, a weighted multinomial model is used. After each regression, cell weights for the response variable are updated from the residuals.

The algorithm proceeds as follows:

1. Missing values are initialised using initialise.

2. Initial cell weights are computed with cellWeights() on all continuous variables in the initialised data.

3. Outer loop (up to maxit iterations):

   • For each variable j with missing values:

     • Form predictor matrix X (all other variables) and response y (variable j).

     • If j is continuous: fit cellIRWLS(X, y, w_cell, w_response) and impute missing values in j using the fitted model plus uncertainty.

     • If j is categorical: fit nnet::multinom() with row weights derived from the cell weight matrix and impute by sampling from predicted probabilities.

     • Update cell weights for j from residuals via cellWeightsFromResiduals().

   • Check convergence: relative change in imputed values falls below eps.

Value

A list with components:

data_imputed	the imputed data.frame.
cellweights	n \times p matrix of final cell weights (1 = clean, 0 = fully downweighted). Categorical columns always have weight 1.
converged	logical indicating whether the outer loop converged.
iterations	number of outer iterations used.

Author(s)

Matthias Templ

References

Templ, M., Kowarik, A. and Filzmoser, P. (2011). Iterative stepwise regression imputation using standard and robust methods. Computational Statistics & Data Analysis, 55(10), 2793–2806.

See Also

imputeCellM, imputeCellEM, initialise, irmi

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

Examples

data(sleep, package = "VIM")
result <- imputeCellIRMI(sleep)
head(result$data_imputed)
image(result$cellweights, main = "Cell weights")

# With Huber weights (less aggressive downweighting)
result2 <- imputeCellIRMI(sleep, method = "huber", trace = TRUE)

# Mixed data example
data(testdata)
result3 <- imputeCellIRMI(testdata$wna)

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