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.
| Version: |
2.22 |
| Depends: |
R (≥ 2.10.0), methods, Rcpp (≥ 0.10.6), lattice |
| Imports: |
MASS, nnet, randomForest, rpart |
| LinkingTo: |
Rcpp |
| Suggests: |
AGD, gamlss, lme4, mitools, nlme, pan, survival, Zelig |
| Published: |
2014-06-11 |
| Author: |
Stef van Buuren [aut, cre],
Karin Groothuis-Oudshoorn [aut],
Alexander Robitzsch [ctb],
Gerko Vink [ctb],
Lisa Doove [ctb],
Shahab Jolani [ctb] |
| Maintainer: |
Stef van Buuren <stef.vanbuuren at tno.nl> |
| License: |
GPL-2 | GPL-3 |
| URL: |
http://www.stefvanbuuren.nl , http://www.multiple-imputation.com |
| NeedsCompilation: |
yes |
| Citation: |
mice citation info |
| Materials: |
NEWS |
| In views: |
Multivariate, OfficialStatistics, SocialSciences |
| CRAN checks: |
mice results |