Welcome to our latest VSNi newsletter, I hope you find the articles informative and useful. Throughout the newsletter there are links to the web site to give you the opportunity to seek further information on these and other aspects of our business. We are lucky to be supported by a committed and loyal user base and I am sure that our newsletters and web site will receive much comment from you as we roll out significant enhancements to the web site over the coming months. VSNi, as ever, is in a period of on-going change, improvement and development. As one of the fastest growing data analysis vendors this will come as no surprise. Once again we have our annual release of GenStat, now 9th Edition, firmly in the market place and receiving good reviews. ASReml too has seen the February launch of version 2 on the wide variety of platforms.2007 will see VSNi very much out and about. We have just returned from the SASA conference in Stellenbosch and now prepare for the Australasian GenStat conference in December. Next year we plan a world-wide road show of events, so if you would like us to come to you, then drop me an e-mail. Over the coming months our timetables will be broadcast, so come along and meet with us.
Stewart Andrews, CEO
Hierarchical Generalized Linear Models HGLMs have been a feature of GenStat’s advanced statistics for several editions now, but until Vision recently their use seems to have been restricted to a small group of specialists. This year, however, that looks set to change.The book Generalized Linear Models with Random Effects: Unified Analysis by H-likelihood by Youngjo Lee, John Nelder & Yudi Pawitan has been published by Chapman & Hall to give a full account of the theory. John and Youngjo’s HGLM algorithms have been reimplemented in a more efficient form, with the assistance of Roger Payne, in the 9th Edition of GenStat for Windows. There is also a menu in the 9th Edition allowing you to access and run many of the examples from the book.Finally, Youngjo and Roger have been running workshops on HGLMs at locations ranging from Spain to South Africa. So, if you need to allow for several sources of random variation but your data are not from a Normal distribution, why not give them a try?
Roger Payne, Chief Science & Technology Officer