Dear R-users, I'm trying to model some data using a tweedie GLM approach. My response variable is the number of pupae that are the offspring of a subordinate wasp on a wasp's nest. However, they're not count data- for each nest, I only know the mean number of pupae per subordinate, which is continous. The data also contain a high proportion of zeros.
I'm not very experienced at statistical modelling, but from reading previous posts, it seems that my data would suit a tweedie approach. I can't use a zero-inflated Poisson model, because my data are not counts. Many of my values are between 0 and 1, so if I rounded to the nearest integer, I'd lose a lot of the variation. Here's my code: out<-tweedie.profile(PUPAE_PER_SUB~1,p.vec=seq(1.1,1.9,length=9),method="interpolation",do.ci=TRUE,do.smooth=TRUE,do.plot=TRUE) tweedie1<-glm(GSA_TOTAL_DF_PERSUB~GROUP_SIZE+PERIOD+SITE+PERIOD*GROUP_SIZE,family=tweedie(var.power=out$p.max,link.power=0)) This worked fine, and gave results I expected, but I don't know what the best method is to evaluate the fit of the model. I am used to using AIC to compare models. A site search turned up AICtweedie, within the tweedie package, but I get the following message: Error: could not find function "AICtweedie" when I try to use this command, even though "tweedie" and "statmod" are both loaded. I've also read that AIC can be calculated using dtweedie, but I'm a beginner and so, despite lots of searching, I'm not sure how. I'm sorry to ask a basic statistics rather than programming question, but I'm really stuck. Could anyone advise me on the best way to assess goodness-of-fit for this type of model, in order to compare models? Thanks -- View this message in context: http://r.789695.n4.nabble.com/AIC-for-tweedie-glm-tp2720813p2720813.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
