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mice: Multivariate Imputation by Chained Equations in R

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  • Stef van Buuren
  • Karin Groothuis-Oudshoorn
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    Abstract

    The R package mice imputes incomplete multivariate data by chained equations. The software mice 1.0 appeared in the year 2000 as an S-PLUS library, and in 2001 as an R package. mice 1.0 introduced predictor selection, passive imputation and automatic pooling. This article documents mice, which extends the functionality of mice 1.0 in several ways. In mice, the analysis of imputed data is made completely general, whereas the range of models under which pooling works is substantially extended. mice adds new functionality for imputing multilevel data, automatic predictor selection, data handling, post-processing imputed values, specialized pooling routines, model selection tools, and diagnostic graphs. Imputation of categorical data is improved in order to bypass problems caused by perfect prediction. Special attention is paid to transformations, sum scores, indices and interactions using passive imputation, and to the proper setup of the predictor matrix. mice can be downloaded from the Comprehensive R Archive Network. This article provides a hands-on, stepwise approach to solve applied incomplete data problems.

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    Bibliographic Info

    Article provided by American Statistical Association in its journal Journal of Statistical Software.

    Volume (Year): 45 ()
    Issue (Month): i03 ()
    Pages:

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    Handle: RePEc:jss:jstsof:45:i03

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    Web page: http://www.jstatsoft.org/

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    1. Daniel Schunk, 2008. "A Markov chain Monte Carlo algorithm for multiple imputation in large surveys," AStA Advances in Statistical Analysis, Springer, vol. 92(1), pages 101-114, February.
    2. White, Ian R. & Daniel, Rhian & Royston, Patrick, 2010. "Avoiding bias due to perfect prediction in multiple imputation of incomplete categorical variables," Computational Statistics & Data Analysis, Elsevier, vol. 54(10), pages 2267-2275, October.
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    Cited by:
    1. David Kaplan & Jianshen Chen, 2012. "A Two-Step Bayesian Approach for Propensity Score Analysis: Simulations and Case Study," Psychometrika, Springer, vol. 77(3), pages 581-609, July.
    2. Feldkircher, Martin, 2012. "The Determinants of Vulnerability to the Global Financial Crisis 2008 to 2009: Credit Growth and Other Sources of Risk," BOFIT Discussion Papers 26/2012, Bank of Finland, Institute for Economies in Transition.
    3. Doove, L.L. & Van Buuren, S. & Dusseldorp, E., 2014. "Recursive partitioning for missing data imputation in the presence of interaction effects," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 92-104.
    4. Thomas Mößle & Sören Kliem & Florian Rehbein, 2014. "Longitudinal Effects of Violent Media Usage on Aggressive Behavior—The Significance of Empathy," Societies, MDPI, Open Access Journal, vol. 4(1), pages 105-124, February.
    5. Christian Seiler, 2013. "Nonresponse in Business Tendency Surveys: Theoretical Discourse and Empirical Evidence," ifo Beiträge zur Wirtschaftsforschung, Ifo Institute for Economic Research at the University of Munich, number 52, July.
    6. Esperanza Vera-Toscano & Fernando Garrido-Fernández & José Gómez-Limón & José Cañadas-Reche, 2013. "Are Theories About Social Capital Empirically Supported? Evidence from the Farming Sector," Social Indicators Research, Springer, vol. 114(3), pages 1331-1359, December.

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