Hi R-listers, I have developed 47 GLM models with different combinations of interactions from 1 variable to 5 variables. I have manually made each model separately and put them into individual tables (organized by the number of variables) showing the AIC score. I want to compare all of these models.
1) What is the best way to compare various models with unique combinations and different number of variables? 2) I am trying to develop the most simplest model ideally. Even though adding another variable would lower the AIC, how do I interpret it is worth it to include another variable in the model? How do I know when to stop? Definitions of Variables: HTL - distance to high tide line (continuous) Veg - distance to vegetation Aeventexhumed - Event of exhumation Sector - number measurements along the beach Rayos - major sections of beach (grouped sectors) TotalEggs - nest egg density Example of how all models were created: Model2.glm <- glm(cbind(Shells, TotalEggs-Shells) ~ Aeventexhumed, data=data.to.analyze, family=binomial) Model7.glm <- glm(cbind(Shells, TotalEggs-Shells) ~ HTL:Veg, family = binomial, data.to.analyze) Model21.glm <- glm(cbind(Shells, TotalEggs-Shells) ~ HTL:Veg:TotalEggs, data.to.analyze, family = binomial) Model37.glm <- glm(cbind(Shells, TotalEggs-Shells) ~ HTL:Veg:TotalEggs:Aeventexhumed, data.to.analyze, family=binomial) Please advise, thanks! J -- View this message in context: http://r.789695.n4.nabble.com/How-do-I-compare-47-GLM-models-with-1-to-5-interactions-and-unique-combinations-tp4326407p4326407.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.