Thread: How to use Akaike's Information Criterion (AIC) for manual model reduction?

1. How to use Akaike's Information Criterion (AIC) for manual model reduction?

Hi all,

I wondered if anybody can explain to me how I can use the AIC for manual reduction of my GLM. I calculate a GLM with the following independent variables (full model):

a
b
c
d
a*b
a*c
a*d
b*c
b*d
c*d
a*b*c
a*b*d
a*c*d
b*c*d

Some of those main effects/interactions went significant, others don't. Is there any stepwise reduction procedure (based on the AIC) I can follow to skip some of the not-significant main effects/interactions?

I already did a quick web-search and always found the information that I can use the AIC to compare two (or more) models (the one with the lowest AIC is the better one) but I am looking for a procedure to reduce my full model similar to the classical stepwise backward reduction procedure based on p-values.

Please don't just suggest R commands like step() or stepAIC(). That would not help since I'm not working with R. But it would probably help if you know what those commands do. Maybe I can perform those procedures manually in my statistic-software.

Thanks!

Fred.

2. Re: How to use Akaike's Information Criterion (AIC) for manual model reduction?

Hello Fred,

Is your goal to get rid of some interactions? If so, there are two comments here:

(A) If your theory says an interaction should be present -- leave it there even if it is non-significant;
(B) If you still want to reduce the number of interactions, consider the guidelines by Aiken, L. S., West, S. G., & Reno, R. R. (1991). Multiple regression: Testing and interpreting interactions. Sage. and exclude those interactions (1) that are non-significant and (2) for which the percent of explained variation (delta R-squared) is non-significant.

As for using AIC, the model with lower value indicates a better fit. As such, if removal of an interaction increases the AIC, then you better keep it and vice versa.

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