MICE

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 32 articles )

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  1. Audigier, Vincent; Husson, François; Josse, Julie: MIMCA: multiple imputation for categorical variables with multiple correspondence analysis (2017)
  2. Groll, Andreas; Tutz, Gerhard: Variable selection in discrete survival models including heterogeneity (2017)
  3. 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)
  4. 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)
  5. De Jong, Roel; van Buuren, Stef; Spiess, Martin: Multiple imputation of predictor variables using generalized additive models (2016)
  6. Hsu, Chiu-Hsieh; He, Yulei; Li, Yisheng; Long, Qi; Friese, Randall: Doubly robust multiple imputation using kernel-based techniques (2016)
  7. Julie Josse, Sylvain Sardy, Stefan Wager: denoiseR: A Package for Low Rank Matrix Estimation (2016) arXiv
  8. Liu, Ying; Wang, Yuanjia; Feng, Yang; Wall, Melanie M.: Variable selection and prediction with incomplete high-dimensional data (2016)
  9. Neeraj Bokde, Kishore Kulat, Marcus W Beck, Gualberto Asencio-Cortes: R package imputeTestbench to compare imputations methods for univariate time series (2016) arXiv
  10. 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
  11. Zhou, Hanzhi; Elliott, Michael R.; Raghunathan, Trviellore E.: A two-step semiparametric method to accommodate sampling weights in multiple imputation (2016)
  12. Bakker, Marjoke; van den Heuvel-Panhuizen, Marja; Robitzsch, Alexander: Effects of playing mathematics computer games on primary school students’ multiplicative reasoning ability (2015) MathEduc
  13. Borgan, Ørnulf; Keogh, Ruth: Nested case-control studies: should one break the matching? (2015)
  14. Haag, Nicole; Roppelt, Alexander; Heppt, Birgit: Effects of mathematics items’ language demands for language minority students: do they differ between grades? (2015) MathEduc
  15. Martin, Daniel P.; Rimm-Kaufman, Sara E.: Do student self-efficacy and teacher-student interaction quality contribute to emotional and social engagement in fifth grade math? (2015) MathEduc
  16. Scherer, Ronny; Siddiq, Fazilat: The big-fish-little-pond-effect revisited: do different types of assessments matter? (2015) MathEduc
  17. Schildcrout, Jonathan S.; Rathouz, Paul J.; Zelnick, Leila R.; Garbett, Shawn P.; Heagerty, Patrick J.: Biased sampling designs to improve research efficiency: factors influencing pulmonary function over time in children with asthma (2015)
  18. Seaman, Shaun R.; Keogh, Ruth H.: Handling missing data in matched case-control studies using multiple imputation (2015)
  19. Hapfelmeier, Alexander; Hothorn, Torsten; Ulm, Kurt; Strobl, Carolin: A new variable importance measure for random forests with missing data (2014)
  20. O’Kelly, Michael: Book review of: S. van Buuren, Flexible imputation of missing data (2014)

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