Can you solve a debate between colleagues?
Hi Joanne (please call me Ben), I'm going to take the liberty of cc'ing this to the r-sig-mixed-model at r-project.org list, since it's of general interest (that's usually a good venue for this kind of question -- you do need to subscribe to post easily, at https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models ). The key clause below is "that are fitted by REML". Since lme4 doesn't use REML for *generalized* linear mixed models as fitted by glmer (indeed, there is not a universally accepted definition of REML for GLMMs: see http://bbolker.github.io/mixedmodels-misc/glmmFAQ.html#reml-for-glmms), the statement you give doesn't apply to GLMMs. cheers Ben Bolker
On 17-07-10 10:33 AM, Joanne Lello wrote:
Dear Prof Bolker, I am a lecturer in Biosciences at Cardiff University where (along with my colleague mentioned below) we do a fair amount of statistics teaching, but we ourselves are not statisticians and have learned ?on the job? as it were. I would like to think we have a reasonable depth of understanding but it is gleaned from many different places and of course sometimes the sources contradict one another. I have recently moved to using glmer in R (previously I used ASREML for my mixed modelling). As a result of reading around the use of this package I came across a number of sources, including your own quote below, which state that using AIC to compare the fixed model is useless. ?...using likelihood-based methods (including AIC) to compare two models with different fixed effects that are fitted by REML will generally lead to nonsense.? I have been debating this point with my colleague; she is convinced that this does not apply if the fixed models being compared are nested. e.g. glmer(y ~ a + b + (1|d), data = dframe1) may be compared via AIC with glmer(y ~ a + (1|d), data = dframe1) but could not be compared with glmer(y ~ a + f + (1|d), data = dframe1) I had read your comments (and others) to mean that AIC could not be used to assess the fixed terms in either scenario. We would be very grateful if you could advise on which interpretation is correct Sincerely Joanne Lello