R package mice: Multivariate Imputation by Chained Equations. Multiple imputation using Fully Conditional Specification (FCS) implemented by the MICE algorithm. Each variable has its own imputation model. Built-in imputation models are provided for continuous data (predictive mean matching, normal), binary data (logistic regression), unordered categorical data (polytomous logistic regression) and ordered categorical data (proportional odds). MICE can also impute continuous two-level data (normal model, pan, second-level variables). Passive imputation can be used to maintain consistency between variables. Various diagnostic plots are available to inspect the quality of the imputations.

References in zbMATH (referenced in 47 articles )

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  1. Audigier, Vincent; Husson, François; Josse, Julie: MIMCA: multiple imputation for categorical variables with multiple correspondence analysis (2017)
  2. Chaojie Wang, Linghao Shen, Han Li, Xiaodan Fan: Efficient Bayesian Nonparametric Inference for Categorical Data with General High Missingness (2017) arXiv
  3. Groll, Andreas; Tutz, Gerhard: Variable selection in discrete survival models including heterogeneity (2017)
  4. Johan Steen and Tom Loeys and Beatrijs Moerkerke and Stijn Vansteelandt: medflex: An R Package for Flexible Mediation Analysis using Natural Effect Models (2017)
  5. Nienkemper-Swanepoel, Johané; von Maltitz, Michael J.: Investigating the performance of a variation of multiple correspondence analysis for multiple imputation in categorical data sets (2017)
  6. Ordóñez Galán, Celestino; Sánchez Lasheras, Fernando; de Cos Juez, Francisco Javier; Bernardo Sánchez, Antonio: Missing data imputation of questionnaires by means of genetic algorithms with different fitness functions (2017)
  7. Sulis, Isabella; Porcu, Mariano: Handling missing data in item response theory. Assessing the accuracy of a multiple imputation procedure based on latent class analysis (2017)
  8. Alexander Kowarik; Matthias Templ: Imputation with the R Package VIM (2016)
  9. Beata Nowok and Gillian Raab and Chris Dibben: synthpop: Bespoke Creation of Synthetic Data in R (2016)
  10. De Jong, Roel; van Buuren, Stef; Spiess, Martin: Multiple imputation of predictor variables using generalized additive models (2016)
  11. Hsu, Chiu-Hsieh; He, Yulei; Li, Yisheng; Long, Qi; Friese, Randall: Doubly robust multiple imputation using kernel-based techniques (2016)
  12. Julie Josse; François Husson: missMDA: A Package for Handling Missing Values in Multivariate Data Analysis (2016)
  13. Julie Josse, Sylvain Sardy, Stefan Wager: denoiseR: A Package for Low Rank Matrix Estimation (2016) arXiv
  14. Liu, Ying; Wang, Yuanjia; Feng, Yang; Wall, Melanie M.: Variable selection and prediction with incomplete high-dimensional data (2016)
  15. Neeraj Bokde, Kishore Kulat, Marcus W Beck, Gualberto Asencio-Cortes: R package imputeTestbench to compare imputations methods for univariate time series (2016) arXiv
  16. Vandecandelaere, Machteld; Vansteelandt, Stijn; De Fraine, Bieke; Van Damme, Jan: The effects of early grade retention: effect modification by prior achievement and age (2016) MathEduc
  17. Zhou, Hanzhi; Elliott, Michael R.; Raghunathan, Trviellore E.: A two-step semiparametric method to accommodate sampling weights in multiple imputation (2016)
  18. Bakker, Marjoke; van den Heuvel-Panhuizen, Marja; Robitzsch, Alexander: Effects of playing mathematics computer games on primary school students’ multiplicative reasoning ability (2015) MathEduc
  19. Borgan, Ørnulf; Keogh, Ruth: Nested case-control studies: should one break the matching? (2015)
  20. Haag, Nicole; Roppelt, Alexander; Heppt, Birgit: Effects of mathematics items’ language demands for language minority students: do they differ between grades? (2015) MathEduc

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