Hello, My data are numbers of trees in plots sampled in a number of forest stands. Some stands were subjected to a treatment, others not. Several plots were sampled per stand to get a better idea of what the stand means were, but replication is really at the stand level. Therefore I think this is a split-plot design.
I would like to know whether the treatment affected the number of trees, so: results<-aov(trees ~ as.factor(treatment) + Error(stand)) However, the frequency distribution of trees is highly skewed, with lots of zeros. I was therefore considering using a generalized linear model, perhaps with Poisson error. However, the glm function does not seem to support adding an Error() term to the model. My question is: is there any way of modelling the experimental design in glm, or should I transform the data as best as I can and stick with aov? Many thanks, Dan Bebber ____________________________ Dr. Daniel P. Bebber Department of Plant Sciences University of Oxford South Parks Road Oxford OX1 3RB Tel. 01865 275060 [[alternative HTML version deleted]] ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html