Dear All, I am analysing a dataset on levels of herbivory in seedlings in an experimental setup in a rainforest. I have seven classes/categories of seedling damage/herbivory that I want to analyse, modelling each separately.
There are twenty maternal trees, with eight groups of seedlings around each. Each tree has a TreeID, which I use as the random effect (blocking factor). There are two fixed effects: DISTANCE - distance to maternal tree; two levels 'CLOSE' or 'AWAY' (four groups of seedlings each per tree), and PLATEAU - whether the maternal tree grows on the 'UPPER' plateau (bad soil) or 'LOWER' plateau (good soil). In each group of seedlings, we randomly selected one seedling where we scored herbivory. Levels of herbivory for each of the seven herbivory categories was scored as proportion of leaves attacked. Obviously, I don't want to use a more complicated model than necessary - but I equally obviously want to take the random effect 'TreeID' into account. Hence, for each herbivory category, I initially fitted a GLMM using the 'glmmPQL' command from the MASS library(after using the 'cbind()' command on the two columns with total number of leaves per seedling and number of leaves attacked by that herbivory category) - and then compared these models to GLMs without the random effect. ## model example1: leaf mines GLMM proportion.leafmines <- cbind(leaves.affected, total.leaves - leaves.affected) leafminesGLMM <- glmmPQL(proportion.leafmines ~ PLATEAU * DISTANCE, random=~1| TreeID, family=binomial(link=logit)) ##AIC(leafminesGLMM) = 474.773 ## model example2: leaf mines GLM leafminesGLM <- glm(proportion.leafmines ~ PLATEAU * DISTANCE, family=binomial(link=logit)) ##AIC(leafminesGLM) = 207.9465 ...and so on, for all seven herbivory categories. In four of the cases, the AIC is much lower (as in the example bove) for the GLMs than for the GLMMs - whereas in three other cases, clearly TreeID is an important random factor, as the AIC values of the GLMs are much higher than the ones for the GLMMs. There is not a big difference in significance levels - some marginally significant ones now become significant, while some significant ones now become marginal. However, there is one complication to simply using the AIC scores to evaluate which model is the best; for almost all the cases where the GLM has the lower AIC, the data are overdispersed, and I need to fit the model with a quasibinomial, rather than with a binomial error structure. BUT - using a GLM with quasibinomial error structure, I of course no longer get an AIC score... -so, my main question is: can I simply use the GLM with quasibinomial error structure instead of the GLMM if the GLM with binomial error structure has a lower AIC score than the GLMM? Any input on how I can compare such models would be greatly appreciated! Dennis ----------------------------------------------------------- Dennis Marinus Hansen Institute of Environmental Sciences University of Zurich Winterthurerstrasse 190 8057 Zurich Switzerland tel: +41 (0) 44635 6122 fax: +41 (0) 44635 5711 www.uwinst.unizh.ch ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html