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R2MLwiN: A Package to Run MLwiN from within R

Author

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  • Zhang, Zhengzheng
  • Parker, Richard M. A.
  • Charlton, Christopher M. J.
  • Leckie, George
  • Browne, William J.

Abstract

R2MLwiN is a new package designed to run the multilevel modeling software program MLwiN from within the R environment. It allows for a large range of models to be specified which take account of a multilevel structure, including continuous, binary, proportion, count, ordinal and nominal responses for data structures which are nested, cross-classified and/or exhibit multiple membership. Estimation is available via iterative generalized least squares (IGLS), which yields maximum likelihood estimates, and also via Markov chain Monte Carlo (MCMC) estimation for Bayesian inference. As well as employing MLwiN's own MCMC engine, users can request that MLwiN write BUGS model, data and initial values statements for use with WinBUGS or OpenBUGS (which R2MLwiN automatically calls via rbugs), employing IGLS starting values from MLwiN. Users can also take advantage of MLwiN's graphical user interface: for example to specify models and inspect plots via its interactive equations and graphics windows. R2MLwiN is supported by a large number of examples, reproducing all the analyses conducted in MLwiN's IGLS and MCMC manuals.

Suggested Citation

  • Zhang, Zhengzheng & Parker, Richard M. A. & Charlton, Christopher M. J. & Leckie, George & Browne, William J., 2016. "R2MLwiN: A Package to Run MLwiN from within R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 72(i10).
    • Handle: RePEc:jss:jstsof:v:072:i10
    DOI: http://hdl.handle.net/10.18637/jss.v072.i10
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    References listed on IDEAS

    as
    1. Leckie, George & Charlton, Chris, 2013. "runmlwin: A Program to Run the MLwiN Multilevel Modeling Software from within Stata," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 52(i11).
    2. Hadfield, Jarrod D., 2010. "MCMC Methods for Multi-Response Generalized Linear Mixed Models: The MCMCglmm R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i02).
    3. Bates, Douglas & Mächler, Martin & Bolker, Ben & Walker, Steve, 2015. "Fitting Linear Mixed-Effects Models Using lme4," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i01).
    4. Browne, William J. & Draper, David & Goldstein, Harvey & Rasbash, Jon, 2002. "Bayesian and likelihood methods for fitting multilevel models with complex level-1 variation," Computational Statistics & Data Analysis, Elsevier, vol. 39(2), pages 203-225, April.
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