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: |
2.17 |
| Depends: |
R (≥ 2.10.0), methods, lattice, MASS, nnet |
| Imports: |
rpart |
| Suggests: |
AGD, gamlss, lme4, mitools, nlme, pan, survival, Zelig |
| Published: |
2013-05-12 |
| Author: |
Stef van Buuren and Karin Groothuis-Oudshoorn, with
contributions from Alexander Robitzsch, Gerko Vink, Shahab
Jolani and Lisa Doove |
| 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: |
no |
| Citation: |
mice citation info |
| In views: |
Multivariate, OfficialStatistics, SocialSciences |
| CRAN checks: |
mice results |