MICE

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 169 articles , 1 standard article )

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  1. Ahfock, Daniel; Pyne, Saumyadipta; McLachlan, Geoffrey J.: Statistical file-matching of non-Gaussian data: a game theoretic approach (2022)
  2. Chan, Kin Wai: General and feasible tests with multiply-imputed datasets (2022)
  3. Gu, Yu; Preisser, John S.; Zeng, Donglin; Shrestha, Poojan; Shah, Molina; Simancas-Pallares, Miguel A.; Ginnis, Jeannie; Divaris, Kimon: Partitioning around medoids clustering and random forest classification for GIS-informed imputation of fluoride concentration data (2022)
  4. Jiang, Wei; Bogdan, Małgorzata; Josse, Julie; Majewski, Szymon; Miasojedow, Błażej; Ročková, Veronika; TraumaBase Group: Adaptive Bayesian SLOPE: model selection with incomplete data (2022)
  5. Page, Garritt L.; Quintana, Fernando A.; Müller, Peter: Clustering and prediction with variable dimension covariates (2022)
  6. Suk, Youmi; Kang, Hyunseung: Robust machine learning for treatment effects in multilevel observational studies under cluster-level unmeasured confounding (2022)
  7. Tong, Hung; Tortora, Cristina: Model-based clustering and outlier detection with missing data (2022)
  8. Uddin, Ajim; Tao, Xinyuan; Chou, Chia-Ching; Yu, Dantong: Are missing values important for earnings forecasts? A machine learning perspective (2022)
  9. Yue, Chao; Xuejun, Ma; Yaguang, Li; Lei, Huang: A penalized estimation for the Cox model with ordinal multinomial covariates (2022)
  10. Yuxuan Zhao, Madeleine Udell: gcimpute: A Package for Missing Data Imputation (2022) arXiv
  11. Zhao, Yang: Diagnostic checking of multiple imputation models (2022)
  12. Ali, Mehboob; Kauermann, Göran: A split questionnaire survey design in the context of statistical matching (2021)
  13. Bertsimas, Dimitris; Orfanoudaki, Agni; Pawlowski, Colin: Imputation of clinical covariates in time series (2021)
  14. Erler, N. S., Rizopoulos, D., Lesaffre, E. M. E. H.: JointAI: Joint Analysis and Imputation of Incomplete Data in R (2021) not zbMATH
  15. Gazzola, Gianluca; Jeong, Myong K.: Support vector regression for polyhedral and missing data (2021)
  16. Javadi, Sara; Bahrampour, Abbas; Saber, Mohammad Mehdi; Garrusi, Behshid; Baneshi, Mohammad Reza: Evaluation of four multiple imputation methods for handling missing binary outcome data in the presence of an interaction between a dummy and a continuous variable (2021)
  17. Kauermann, Göran; Ali, Mehboob: Semi-parametric regression when some (expensive) covariates are missing by design (2021)
  18. Michał Narajewski, Jens Kley-Holsteg, Florian Ziel: tsrobprep - an R package for robust preprocessing of time series data (2021) arXiv
  19. Mulder, J., Williams, D. R., Gu, X., Tomarken, A., Böing-Messing, F., Olsson-Collentine, A., Meijerink-Bosman, M., Menke, J., van Aert, R., Fox, J.-P., Hoijtink, H., Rosseel, Y., Wagenmakers, E.-J., van Lissa, C.: BFpack: Flexible Bayes Factor Testing of Scientific Theories in R (2021) not zbMATH
  20. Nascimben, Mauro; Venturin, Manolo; Rimondini, Lia: Double-stage discretization approaches for biomarker-based bladder cancer survival modeling (2021)

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