In addition to fitting hierarchical generalized linear mixed models it also allows fitting non-linear ones. Nlme is the most mature one and comes by default with any R installation. There are also several options for Bayesian approaches, but that will be another post. I will only mention nlme (Non-Linear Mixed Effects), lme4 (Linear Mixed Effects) and asreml (average spatial reml). OptionsĪs for many other problems, there are several packages in R that let you deal with linear mixed models from a frequentist (REML) point of view. At the end of 2005 I started using OS X and quickly realized that using a virtual machine or dual booting was not really worth it, so I dropped SAS and totally relied on R in 2009. Around 1999, I started playing with R (prompted by a suggestion from Rod Ball), but I didn’t really use R/S+ often enough until 2003. I was still using SAS for data preparation, but all my analyses went through ASReml (for which I wrote the cookbook), which was orders of magnitude faster than SAS (and could deal with much bigger problems). At the end of 1996 (or was it the beginning of 1997) I started playing with ASReml (programed by Arthur Gilmour mostly based on theoretical work by Robin Thompson and Brian Cullis). At that time (1995-1996) I moved to DFREML (by Karen Meyer, now replaced by WOMBAT) and AIREML (by Dave Johnson, now defunct-I mean the program), which were designed for the analysis of animal breeding progeny trials, so it was a hassle to deal with experimental design features. A brief history of timeĪt the beginning (1992-1995) I would use SAS (first proc glm, later proc mixed), but things started getting painfully slow and limiting if one wanted to move into animal model BLUP. The bulk of my use of mixed models relates to the analysis of experiments that have a genetic structure. A detailed account of the new functionality is presented in a navigation guide which guides existing users in transitioning from Version 3 to Version 4.A substantial part of my job has little to do with statistics nevertheless, a large proportion of the statistical side of things relates to applications of linear mixed models.
#Asreml r full#
This is just a selection from the full set of new features available with ASReml-R Version 4.
#Asreml r software#
ASReml is powerful statistical software specially designed for mixed models using Residual Maximum Likelihood (REML) to estimate the parameters.