Multiple imputation is a tool for parameter estimation and inference with partially observed data, which is used increasingly widely in medical and social research. When the data to be imputed are correlated or have a multilevel structure — repeated observations on patients, school children nested in classes within schools within educational districts — the imputation model needs to include this structure. Here we introduce our joint modelling package for multiple imputation of multilevel data, jomo, which uses a multivariate normal model fitted by Markov Chain Monte Carlo (MCMC). Compared to previous packages for multilevel imputation, e.g. pan, jomo adds the facility to (i) handle and impute categorical variables using a latent normal structure, (ii) impute level-2 variables, and (iii) allow for cluster-specific covariance matrices, including the option to give them an inverse-Wishart distribution at level 2. The package uses C routines to speed up the computations and has been extensively validated in simulation studies both by ourselves and others.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2019-028.zip
jomo, pan, norm, cat, mix, R2MLwiN, mitools, mice, semTools, BaBooN, mitml, Amelia, mi, lavaan.survey, dummies, nlme
MissingData, OfficialStatistics, SocialSciences, Multivariate, Psychometrics, Bayesian, ChemPhys, Econometrics, Environmetrics, Finance, Spatial, SpatioTemporal
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For attribution, please cite this work as
Quartagno, et al., "jomo: A Flexible Package for Two-level Joint Modelling Multiple Imputation", The R Journal, 2019
BibTeX citation
@article{RJ-2019-028,
author = {Quartagno, Matteo and Grund, Simon and Carpenter, James},
title = {jomo: A Flexible Package for Two-level Joint Modelling Multiple Imputation},
journal = {The R Journal},
year = {2019},
note = {https://doi.org/10.32614/RJ-2019-028},
doi = {10.32614/RJ-2019-028},
volume = {11},
issue = {2},
issn = {2073-4859},
pages = {205-228}
}