Le mardi 24 janvier 2012 à 20:41 -0800, Jhope a écrit : > 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? See ?step or ?stepAIC (from package MASS) if you want an automated way of doing this.
> 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? This is a general statistical question, not specific to R. As a general rule, if adding a variable lowers the AIC by a significant margin, then it's worth including it. You should only stop when a variable increases the AIC. But this is assuming you consider it a good indicator and you know what you're doing. There's plenty of literature on this subject. > 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) To extract the AICs of all these models, it's easier to put them in a list and get their AICs like this: m <- list() m$model2 <- glm(cbind(Shells, TotalEggs-Shells) ~ Aeventexhumed, data=data.to.analyze, family=binomial) m$model3 <- glm(cbind(Shells, TotalEggs-Shells) ~ HTL:Veg, family = binomial, data.to.analyze) sapply(m, extractAIC) Cheers ______________________________________________ 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.
