Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Yu-Sung Su, Andrew Gelman, Jennifer Hill, Masanao Yajima
Title: Multiple Imputation with Diagnostics (mi) in R: Opening Windows into the Black Box
Abstract: 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.

Page views:: 16967. Submitted: 2009-06-15. Published: 2011-12-12.
Paper: Multiple Imputation with Diagnostics (mi) in R: Opening Windows into the Black Box     Download PDF (Downloads: 15925)
mi_0.09-13.tar.gz: R source package Download (Downloads: 1041; 64KB)
v45i02.R: R example code from the paper Download (Downloads: 1168; 1KB)

DOI: 10.18637/jss.v045.i02

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.