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 22 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. Julie Josse, Sylvain Sardy, Stefan Wager: denoiseR: A Package for Low Rank Matrix Estimation (2016) arXiv
  4. 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
  5. Bakker, Marjoke; van den Heuvel-Panhuizen, Marja; Robitzsch, Alexander: Effects of playing mathematics computer games on primary school students’ multiplicative reasoning ability (2015) MathEduc
  6. Borgan, Ørnulf; Keogh, Ruth: Nested case-control studies: should one break the matching? (2015)
  7. Haag, Nicole; Roppelt, Alexander; Heppt, Birgit: Effects of mathematics items’ language demands for language minority students: do they differ between grades? (2015) MathEduc
  8. 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
  9. Scherer, Ronny; Siddiq, Fazilat: The big-fish-little-pond-effect revisited: do different types of assessments matter? (2015) MathEduc
  10. Hapfelmeier, Alexander; Hothorn, Torsten; Ulm, Kurt; Strobl, Carolin: A new variable importance measure for random forests with missing data (2014)
  11. O’Kelly, Michael: Book review of: S. van Buuren, Flexible imputation of missing data (2014)
  12. Pilar Muñoz, M.: Comments on: Space-time wind speed forecasting for improved power system dispatch (2014)
  13. Yang, Fan; Lorch, Scott A.; Small, Dylan S.: Estimation of causal effects using instrumental variables with nonignorable missing covariates: application to effect of type of delivery NICU on premature infants (2014)
  14. Pannekoek, Jeroen; Shlomo, Natalie; De Waal, Ton: Calibrated imputation of numerical data under linear edit restrictions (2013)
  15. Wollschläger, Daniel: R compact. The fast introduction into data analysis (2013)
  16. Hapfelmeier, A.; Hothorn, T.; Ulm, K.: Recursive partitioning on incomplete data using surrogate decisions and multiple imputation (2012)
  17. Kaplan, David; Chen, Jianshen: A two-step Bayesian approach for propensity score analysis: simulations and case study (2012)
  18. Siddique, Juned; Harel, Ofer; Crespi, Catherine M.: Addressing missing data mechanism uncertainty using multiple-model multiple imputation: application to a longitudinal clinical trial (2012)
  19. van Buuren, Stef: Flexible imputation of missing data. (2012)
  20. Schoone, M.; Dusseldorp, E.; van den Akker-van Marle, M.E.; Doornebosch, A.J.; Bal, R.; Meems, A.; Oderwald, M.P.; van Balen, R.: Stroke rehabilitation in frail elderly with the robotic training device ACRE: a randomized controlled trial and cost-effectiveness study (2011) ioport

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