Two comments: 1) The log-likelihood and hence AIC for a model for log X are not comparable with those of a model for X. You need to make an additive adjustment when you transform: it is quite easy to work out what from the definitions.
2) The AIC given by glm() for weighted models was wrong in R < 2.3.0 alpha. I am not sure why you are using a glm for what appears to be a least-squares fit: use lm() instead (or try 2.3.0 alpha). On Wed, 15 Mar 2006, Patrick Baker wrote: > Hi there, I have a question regarding model comparisons that seems simple > enough but to which I cannot find an answer. I am interested in developing a > predictive model relating some measure of a tree's stem to the total leaf > area (TLA) of the tree. Predictor variables might include, for example, the > total cross-sectional area of the tree (commonly referred to as basal area) > or the amount of sapwood area (SA) (which represents the amount of wood > involved in active transport of water up the tree to the leaves). A variety > of people have developed these models for a variety of tree species in a > variety of places around the world. Perhaps not surprisingly, different > studies have used different model forms in analyzing their data. I am > interested in comparing the range of models that have been previously used > (some of which are theoretically derived, others of which are empirically > driven) using a data set that I have collected (for yet another species in > yet another place). To compare the different model forms I had intended to > use the AIC. However, I have found, again perhaps not surprisingly, that when > I use log-transformed data, the AIC is substantially lower for a given > predictor variable. If I use a weighted glm the same issue arises. For > example, using BA vs TLA the (rounded) AIC values are 275 for a linear > model, 30 for a log-log model, and 8 for a glm weighted by 1/BA. I don't > believe that these vast differences reflect a major improvement in the model, > but rather the scaling of the variables by transformation or weighting. What > I'd like to get some advice or insight on is whether there is an appropriate > way to rescale the AIC values to permit comparisons across these models. Any > suggestions would be very welcome. Cheers, Patrick Baker > -- Brian D. Ripley, [EMAIL PROTECTED] Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595 ______________________________________________ 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