mice: Multivariate Imputation by Chained Equations
Version 2.30

Multiple imputation using Fully Conditional Specification (FCS) implemented by the MICE algorithm as described in Van Buuren and Groothuis-Oudshoorn (2011) . 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.

Package details

AuthorStef 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]
Date of publication2017-02-18 22:39:19
MaintainerStef van Buuren <stef.vanbuuren@tno.nl>
LicenseGPL-2 | GPL-3
URL http://www.stefvanbuuren.nl http://www.multiple-imputation.com
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:

Try the mice package in your browser

Any scripts or data that you put into this service are public.

mice documentation built on May 29, 2017, 11:44 p.m.