[R-sig-ME] compound symmetry correlation structure in nlme

[Rian Dickson]

hello all,

I am analyzing some data with repeated measures on individuals, and want to test different correlation structures.
In Zuur et al (2007) they state "the inclusion of a random intercept in a GLM is imposing the compound symmetrical correlation structure, just as it did in the linear mixed model" (Chapter 13, p.323), which I understand as meaning that if a random intercept is included in a model, then the compound symmetry correlation structure is implied.

When I run the following two models (which are identical, except that in the second one I explicitly specify the correlation structure), I get the same estimates for the random and fixed effects, but the AIC and BIC values differ, because the second model has one more parameter (for the correlation structure).

global.ri.lme <- lme(Log.minH ~ site + f.year + cohort + emerg + emergSQ + pri + priSQ
  + start.t + startSQ + sea + tidem + tided, data = SUSCforage, random = ~1|scoterID,
  method = "REML")
 
global.ri.cs1.lme <- lme(Log.minH ~ site + f.year + cohort + emerg + emergSQ + pri + priSQ
  + start.t + startSQ + sea + tidem + tided, data = SUSCforage, random = ~1|scoterID,
  correlation = corCompSymm(form = ~1|scoterID), method = "REML")

So, if I want to obtain an accurate AIC value, which should I use?

thank you,

Rian

*************************************

Rian Dickson
M.Sc. candidate
Centre for Wildlife Ecology
Department of Biological Sciences
Simon Fraser University
8888 University Drive
Burnaby BC V5A 1S6
778.782.5618