CALIBERrfimpute (version 1.0-8)

CALIBERrfimpute-package: Imputation in MICE using Random Forest

Description

Multivariate Imputation by Chained Equations (MICE) is commonly used to impute missing values in analysis datasets using full conditional specifications. However, it requires that the predictor models are specified correctly, including interactions and nonlinearities. Random Forest is a regression and classification method which can accommodate interactions and non-linearities without requiring a particular statistical model to be specified.

Author

Anoop Shah

Maintainer: [e-mail]

Details

CALIBERrfimpute provides functions that can be used as imputation functions with mice. Note that the mice package itself provides the mice.impute.rf function for imputation using Random Forest, as of version 2.20. The CALIBERrfimpute package provides different, independently developed imputation functions using Random Forest in MICE.

This package contains reports of two simulation studies:

Simulation study (linear regression):

A comparison of Random Forest and parametric MICE in a linear regression example. To open this report, run: utils::browseURL(system.file("doc", "simstudy.pdf", package = "CALIBERrfimpute"))

Survival analysis with interactions (large sample):

Compares the Random Forest MICE algorithm (mice.impute.rfcont) with parametric MICE and the algorithms of Doove et al. (mice.impute.cart and mice.impute.rf). To open this report, run: utils::browseURL(system.file("doc", "simstudy_survival_largesample.pdf", package = "CALIBERrfimpute"))

Survival analysis with interactions (small sample):

A small sample version of the above simulation as a vignette. See vignette("simstudy_survival", package = "CALIBERrfimpute")

Package:	CALIBERrfimpute
Type:	Package
Version:	1.0-8
Date:	2026-02-17
License:	GPL-3

References

Shah AD, Bartlett JW, Carpenter J, Nicholas O, Hemingway H. Comparison of Random Forest and parametric imputation models for imputing missing data using MICE: a CALIBER study. American Journal of Epidemiology 2014; 179(6): 764--774. tools:::Rd_expr_doi("10.1093/aje/kwt312")

Doove LL, van Buuren S, Dusseldorp E. Recursive partitioning for missing data imputation in the presence of interaction effects. Computational Statistics and Data Analysis 2014; 72: 92--104. tools:::Rd_expr_doi("10.1016/j.csda.2013.10.025")

See Also

mice, randomForest, mice.impute.rfcont, mice.impute.rfcat, mice.impute.rf