Hi there, Id like to use AIC to compare between models with different error distributions (eg: Dick 2004, Sileshi 2004, Burnham and Anderson 2002), namely a normal, Poisson and negative binomial. I realize there are differing views whether this is valid or not from reading past R help postings; however, for my purpose I think AIC is more appropriate rather than something such as a Chi-sq or G-statistic as I dont need to know whether the fit is statistically significant or not, rather I want to know which model is the best given my data.
The data Im working on are counts per station (7 stations in total for each model), and originally I used a simplistic glm model: Model.p<-glm(count~station,poisson) Model.n<-glm(count~station,gaussian) And from the MASS package (v 7.2-30) Model.nb<-glm.nb(count~station) I then extracted the log-likelihood using logLik(model), from which I calculated AIC (by hand). However, after reviewing more of the R help postings and associated help pages for the functions, I have the following questions: 1- the glm function doesnt use MLE to fit the model, so is the associated logLik extracted valid? 2- If it is valid, does it calculate the full likelihood, or are the constants dropped? (this is not clear in the ?glm or ?loglik files) 3- if neither are valid, are there alternatives? For example, Ive seen that the MASS package also has a fit.distr function with an associated logLik method, but can I use the log-likelihood extracted using this method to calculate AIC and compare between distributions (in the manner that I want using the glm function)? if so, are the log-likelihood given complete or have the constants been dropped? Any help and suggestions would be appreciated! Kelly Young [EMAIL PROTECTED] M.Sc Candidate, Dept. Biology Fisheries Oceanography Research Lab University of Victoria .·.><((((°> ______________________________________________ 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.
