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Overview

2026-05-04

Source: vignettes/overview.Rmd
overview.Rmd

Software

  1. mice package at CRAN
  2. mice GitHUB repository

Installation

The mice package can be installed from CRAN as follows:

The latest version can be installed from GitHub as follows:

install.packages("devtools")
devtools::install_github("amices/mice")

Capabilities of mice package

The mice package contains functions to

  • Inspect the missing data pattern
  • Impute the missing data mm times, resulting in mm completed data sets
  • Diagnose the quality of the imputed values
  • Analyze each completed data set
  • Pool the results of the repeated analyses
  • Store and export the imputed data in various formats
  • Generate simulated incomplete data
  • Incorporate custom imputation methods
  • Choose which cells to impute

Main functions

The main functions in the mice package are:

Function name Description
mice() Impute the missing data mm times
with() Analyze completed data sets
pool() Combine parameter estimates
complete() Export imputed data
ampute() Generate missing data

Course materials

  1. Handling Missing Data in R with mice
  2. Statistical Methods for combined data sets

Vignettes

  1. Ad hoc methods and the MICE algorithm
  2. Convergence and pooling
  3. Inspecting how the observed data and missingness are related
  4. Passive imputation and post-processing
  5. Combining inferences
  6. Imputing multilevel data
  7. Sensitivity analysis with mice
  8. Generate missing values with ampute
  9. parlMICE: Parallel MICE imputation wrapper
  10. futuremice: Wrapper for parallel MICE imputation through futures

Packages that extend the functionality of mice include:

  1. ImputeRobust: Multiple Imputation with GAMLSS
  2. countimp: Incomplete count data
  3. miceadds: Functions for multilevel imputation
  4. micemd: Functions for multilevel imputation
  5. smcfcs: Addressing incompatibility in selected models
  6. fancyimpyute: MICE in Python for ordinal data

Further reading

  1. mice: Multivariate Imputation by Chained Equations in R in the Journal of Statistical Software (van Buuren and Groothuis-Oudshoorn 2011).
  2. The first application on missing blood pressure data (van Buuren et al. 1999).
  3. Term Fully Conditional Specification describes a general class of methods that specify imputations model for multivariate data as a set of conditional distributions (van Buuren et al. 2006).
  4. Details about imputing mixes of numerical and categorical data can be found in (van Buuren 2007).
  5. Book Flexible Imputation of Missing Data. Second Edition (van Buuren 2018).

Code from publications

  1. R code from Flexible Imputation of Missing Data. Second Edition
  2. R code from mice: Multivariate Imputation by Chained Equations in R

References

van Buuren, S. 2007. “Multiple Imputation of Discrete and Continuous Data by Fully Conditional Specification.” Statistical Methods in Medical Research 16 (3): 219–42. https://doi.org/10.1177/0962280206074463.
van Buuren, S., H. C. Boshuizen, and D. L. Knook. 1999. “Multiple Imputation of Missing Blood Pressure Covariates in Survival Analysis.” Statistics in Medicine 18 (6): 681–94. https://doi.org/10.1002/(sici)1097-0258(19990330)18:6<681::aid-sim71>3.0.co;2-r.
van Buuren, S., J. P. L. Brand, C. G. M. Groothuis-Oudshoorn, and D. B. Rubin. 2006. “Fully Conditional Specification in Multivariate Imputation.” Journal of Statistical Computation and Simulation 76 (12): 1049–64. https://doi.org/10.1080/10629360600810434.
van Buuren, S., and K. Groothuis-Oudshoorn. 2011. “mice: Multivariate Imputation by Chained Equations in R.” Journal of Statistical Software 45 (3): 1–67. https://doi.org/10.18637/jss.v045.i03.
van Buuren, Stef. 2018. Flexible Imputation of Missing Data. 2nd ed. Interdisciplinary Statistics Series. Chapman and Hall/CRC. https://doi.org/10.1201/9780429492259.