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

[Greg Snow]

What variables to consider adding and when to stop adding them depends greatly upon what question(s) you are trying to answer and the science behind your data.

Are you trying to create a model to predict your outcome for future predictors?  How precise of predictions are needed?

Are you trying to understand how certain predictors relate to the response? How they relate after conditioning on other predictors?

Will humans be using your equation directly? Or will it be in a black box that the computer generates predictions from but people never need to look at the details?

What is the cost (money, time, difficulty, etc.) of collecting the different predictors?

Answers to the above questions will be much more valuable in choosing the "best" model than AIC or other values (though you should still look at the results from analyses for information to combine with the other information).  R and its programmers (no matter how great and wonderful they are) cannot answer these for you.

-- 
Gregory (Greg) L. Snow Ph.D.
Statistical Data Center
Intermountain Healthcare
greg.snow at imail.org
801.408.8111


> -----Original Message-----
> From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-
> project.org] On Behalf Of Jhope
> Sent: Thursday, January 26, 2012 2:26 PM
> To: r-help at r-project.org
> Subject: Re: [R] How do I compare 47 GLM models with 1 to 5
> interactions and unique combinations?
> 
> I ask the question about when to stop adding another variable even
> though it
> lowers the AIC because each time I add a variable the AIC is lower. How
> do I
> know when the model is a good fit? When to stop adding variables,
> keeping
> the model simple?
> 
> Thanks, J
> 
> --
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> compare-47-GLM-models-with-1-to-5-interactions-and-unique-combinations-
> tp4326407p4331848.html
> Sent from the R help mailing list archive at Nabble.com.
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