[R] How do I compare 47 GLM models with 1 to 5 interactions and unique combinations?

[Frank Harrell]

If you are trying to destroy all aspects of statistical inference this is a
good way to go.  This is also a good way to ignore the subject matter in
driving model selection.
Frank

Jhope wrote
> 
> 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
> 


-----
Frank Harrell
Department of Biostatistics, Vanderbilt University
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