Multiple imputation with diagnostics (mi) in R: Opening windows into the black box

Yu Sung Su, Andrew Gelman, Jennifer Hill, Masanao Yajima

Research output: Contribution to journalArticlepeer-review


Our mi package in R has several features that allow the user to get inside the imputation process and evaluate the reasonableness of the resulting models and imputations. These features include: choice of predictors, models, and transformations for chained imputation models; standard and binned residual plots for checking the fit of the conditional distributions used for imputation; and plots for comparing the distributions of observed and imputed data. In addition, we use Bayesian models and weakly informative prior distributions to construct more stable estimates of imputation models. Our goal is to have a demonstration package that (a) avoids many of the practical problems that arise with existing multivariate imputation programs, and (b) demonstrates state-of-the-art diagnostics that can be applied more generally and can be incorporated into the software of others.

Original languageEnglish (US)
Pages (from-to)1-31
Number of pages31
JournalJournal of Statistical Software
Issue number2
StatePublished - Dec 2011


  • Chained equations
  • Mi
  • Model diagnostics
  • Multiple imputation
  • R
  • Weakly informative prior

ASJC Scopus subject areas

  • Software
  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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