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.
Highlights
In research fields as diverse as education, economics, medicine, psychology, and biology, it is commonplace to encounter data which are clustered: for example, exam results from many students across a smaller number of schools in a cross-sectional study, or clinical measurements taken repeatedly from the same individuals in a longitudinal study
When fitting this model below, note that we have chosen to include mcmcMeth = list(lclo = 1) in our list of estoptions so that MLwiN will fit the log of the precision (1/variance) at level 1 as a function of the predictors; since this can take any value on the real line, we are free of the restrictions on prior distributions which result from fitting the variance as a linear function of the predictors: R> F5 standlrtC1V_MCMC
This paper has introduced R2MLwiN, a new package which calls the multilevel modeling software MLwiN from within the R environment
Summary
In research fields as diverse as education, economics, medicine, psychology, and biology, it is commonplace to encounter data which are clustered: for example, exam results from many students across a smaller number of schools in a cross-sectional study, or clinical measurements taken repeatedly from the same individuals in a longitudinal study. Multilevel models ( known as mixed models, random effects models, hierarchical models, etc.) achieve this by treating the units at each level (in the above examples: students and schools, measurement occasion and individuals, respectively), as a random sample from a larger population with an assumed distribution, partitioning the residual variance between levels
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