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Dear all, How does the effect() function in the effects package calculate effects and standard errors for glm quasipoisson models? I was using effect() to calculate the impact of increasing x to e + epsilon, and then finding the expected percent change. I thought that this effect (as a percentage) should be exp(beta*epsilon), where beta is the appropriate coefficient from the model, but

dear helplist, my student has fifty trees, numbered one to fifty, and a vector recording which tree a certain possum slept in on 12 nights. R> c [1] 3 14 17 22 26 26 17 40 43 25 46 46 R> Thus it slept in tree #3 on Monday, then tree #14 on Tues, and so on. I wish to test the null hypothesis that the animal chooses trees randomly; try R> table(c) c 3 14 17 22 25 26 40 43 46 1 1