Projects per year
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 generalised 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.
|Number of pages||43|
|Journal||Journal of Statistical Software|
|Publication status||Published - 8 Sep 2016|
- Jean Golding
- multilevel model
- random effects model
- mixed model
- hierarchical linear model
- clustered data
- maximum likelihood estimation
- Markov chain Monte Carlo estimation
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- 1 Finished
The use of interactive electronic-books in the teaching and application of modern, quantitative social science methods
Browne, W. J., Goldstein, H., Jones, K., Leckie, G. B., Moreau, L., Parker, R. M. A. & Michaelides, D.
1/10/13 → 30/09/17
Dr Richard M A Parker
- Bristol Medical School (PHS) - Senior Research Associate in Applied Statistics / Epidemiology