Multiple imputation using Fully Conditional Specification (FCS)
implemented by the MICE algorithm as described in Van Buuren and
Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>. 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.
| Version: |
2.30 |
| Depends: |
methods, R (≥ 2.10.0) |
| Imports: |
lattice, grDevices, graphics, MASS, nnet, rpart, splines, stats, survival, utils, Rcpp |
| LinkingTo: |
Rcpp |
| Suggests: |
AGD, CALIBERrfimpute, gamlss, lme4, mitools, nlme, pan, randomForest, Zelig, BSDA, knitr, rmarkdown |
| Published: |
2017-02-18 |
| Author: |
Stef van Buuren [aut, cre],
Karin Groothuis-Oudshoorn [aut],
Alexander Robitzsch [ctb],
Gerko Vink [ctb],
Lisa Doove [ctb],
Shahab Jolani [ctb],
Rianne Schouten [ctb],
Philipp Gaffert [ctb],
Florian Meinfelder [ctb] |
| Maintainer: |
Stef van Buuren <stef.vanbuuren at tno.nl> |
| BugReports: |
https://github.com/stefvanbuuren/mice/issues |
| License: |
GPL-2 | GPL-3 |
| URL: |
http://www.stefvanbuuren.nl , http://www.multiple-imputation.com |
| NeedsCompilation: |
yes |
| Citation: |
mice citation info |
| Materials: |
README NEWS |
| In views: |
Multivariate, OfficialStatistics, SocialSciences |
| CRAN checks: |
mice results |