## Specifying the model

This is the second part of a series of notes demonstrating use of the R package, R2MLwiN, an R command interface to the multilevel modelling software package, MLwiN (see the

MLwiN site for getting access to MLwiN). The first set of notes showed how to get started with R2MLwiN. In these notes, I show how to fit predictors (continuous, categorical, and interactions) to the fixed-effects part of a multilevel regression model, and how to fit random slopes to the regression model. The examples use the ALNT.csv data (see

Working with R2MLwiN Part 1 for a description of the data). Though the series is concerned with demonstrating Bayesian estimation using MCMC methods, the examples presented here do not depend on MCMC methods of estimations, and so to speed up the running of the examples, they use maximum likelihood estimation. It is easy enough to switch between maximum likelihood and MCMC procedures; set the

`estM`

option to 1 for MCMC, and 0 for maximum likelihood.