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

Ben Bolker bbolker at gmail.com
Wed Jan 25 15:40:58 CET 2012

```Rubén Roa <rroa <at> azti.es> writes:

> A more 'manual' way to do it is this.

> If you have all your models named properly and well organized, say
> Model1, Model2, ..., Model 47, with a slot with the AIC (glm
> produces a list with one component named 'aic') then something like
> this:

> x <- matrix(0,1081,3) x[,1:2] <- t(combn(47,2)) for(i in 1:n){x[,3]
> <- abs(unlist(combn(eval(as.name(paste("Model",i,sep="")))\$aic[1,])-
> unlist(combn(eval(as.name(paste("Model",i,sep="")))\$aic,2)[2,]))}

> will calculate all the 1081 AIC differences between paired
> comparisons of the 47 models. It may not be pretty but works for me.

> An AIC difference equal or less than 2 is a tie, anything higher is
> evidence for the model with the less AIC (Sakamoto et al., Akaike
> Information Criterion Statistics, KTK Scientific Publishers, Tokyo).

I wouldn't go quite as far as Frank Harrell did in his answer, but

(1) it seems silly to do all pairwise comparisons of models; one of
the big advantages of information-theoretic approaches is that you can
just list the models in order of AIC ...

(2) the dredge() function from the MuMIn package may be useful for
automating this sort of thing.  There is an also an AICtab function in
the emdbook package.

(3) If you're really just interested in picking the single model with
the best expected predictive capability (and all of the other
assumptions of the AIC approach are met -- very large data set, good
fit to the data, etc.), then just picking the model with the best AIC
will work.  It's when you start to make inferences on the individual
parameters within that model, without taking account of the model
selection process, that you run into trouble.  If you are really
concerned about good predictions then it may be better to do
multi-model averaging *or* use some form of shrinkage estimator.

(4) The "delta-AIC<2 means pretty much the same" rule of thumb is
fine, but it drives me nuts when people use it as a
quasi-significance-testing rule, as in "the simpler model is adequate
if dAIC<2".  If you're going to work in the model selection arena,
then don't follow hypothesis-testing procedures!  A smaller AIC means
lower expected K-L distance, period.

For the record, Brian Ripley has often expressed the (minority)
opinion that AIC is *not* appropriate for comparing non-nested models
(e.g.  <http://tolstoy.newcastle.edu.au/R/help/06/02/21794.html>).

>
> -----Original Message-----
> From: r-help-bounces <at> r-project.org on behalf of Milan Bouchet-Valat
> Sent: Wed 1/25/2012 10:32 AM
> To: Jhope
> Cc: r-help <at> r-project.org

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

> 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,

No, not necessarily.

> how do I interpret it is worth
> > it to include another variable in the model? How do I know when to stop?

When the AIC stops decreasing.

> 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.

If the variable lowers the AIC *at all* then your best estimate is that
you should include 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
>

```