An Alternative Package for Estimating Multivariate Generalised Linear Mixed Models in R

There are several packages at [1] that have been specially written for estimating Generalised Linear Mixed Models in R. These include lme4 [2] and npmlreg [3]. There are also commercial systems that have algorithms for the same class of models, see e.g. Stata [4], gllamm [5] and SAS [6]. In this presentation we compare the performance of these systems with our alternative (sabreR, to be available from [7]) on some standard small-to-medium sized data sets and show that our alternative is very much faster. We also present a grid enabled version of the software (SabreRgrid) which shows how easy it has become to submit grid jobs from the desktop PC and the extra speed-up that can be obtained by going parallel on a High Performance Computer on the grid or otherwise. This extra speed-up is particularly important for estimating complex models on very large data sets. SabreR is a program for the statistical analysis of multi-process event/response sequences. These responses can take the form of binary, ordinal, count and linear recurrent events. The response sequences can also be combinations of different types (e.g. linear (wages) and binary (trade union membership)). Such multi-process data are common in many research areas, e.g. in the analysis of work and life histories from the British Household Panel Survey or the German Socio-Economic Panel Study where researchers often want to disentangle state dependence (the effect of previous responses or related outcomes) from any omitted effect