First off, thanks to all who have responded to the series of questions I asked!
On Fri, Aug 29 2008, Ken Beath wrote:
On 29/08/2008, at 2:47 PM, Austin Frank wrote:
3) Is it the case that LR tests between REML models with different random effects are meaningful? Does this apply to both nested and non-nested models?
Maybe, but only for nested (see Q2). Supposedly it works better than ML. The significance tests wont be correct but if there is a huge significance level then there is probably a random effect. Simulation seems a better idea.
Ken was the only one to address this particular point, and I want to make sure I've got it straight. Are REML-based likelihood-ratio tests (presumably not performed with anova.mer, as that sets REML=FALSE on the call to logLik) an acceptable method for testing nested models with different random effects specifications? As a point of reference, the anova() method is called on two lmer models that differ only in their random effects in the manuscript by Baayen, Davidson, and Bates at http://www.ualberta.ca/~baayen/publications/baayenDavidsonBates.pdf (pp 12-15). The discussion of that analysis makes no mention of the difference between REML and ML fits. Is this because, as discussed recently, the REML and ML estimates are so close that there is no practical difference in which quantity is used for this test? Thanks again! /au
Austin Frank http://aufrank.net GPG Public Key (D7398C2F): http://aufrank.net/personal.asc