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Date:   Tue, 3 Apr 2007 07:06:06 -0700
Reply-To:   Shawn Haskell <shawn.haskell@TTU.EDU>
Sender:   "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From:   Shawn Haskell <shawn.haskell@TTU.EDU>
Organization:   http://groups.google.com
Subject:   Re: an easy one: overfitting in proc mixed
Comments:   To: sas-l@uga.edu
In-Reply-To:   <1175538174.583389.291640@y66g2000hsf.googlegroups.com>
Content-Type:   text/plain; charset="iso-8859-1"

On Apr 2, 1:22 pm, val_har...@hotmail.com wrote: > Hello! > > Just to be sure of myself... > > There are 1190 observation in my dataset but those observations are > repeated measure on 17 individuals which was follow using satellite > telemetry during 7 months. I consider individual as my experimental > unit (instead of the number of observation) and I used proc mixed (and > Akaike information criterion) determine the factors (month, sex and > age class of individual) that best explained the variation in the > data. To account for repeated measures on the same individual, > inindividual' was considered as repeat and random factors in the > models. > > How many varaible may I fite, at maximum, in my model to avoid > overfitting? 3 (which including the interaction)?? > thank you > val.

It is good you are using a mixed model for clustered data. In theory, the AICc stats tell you when you have overfit - the model with lowest AICc is the best fit parsimonious model. If the fully reduced intercept-only model has the lowest AICc then none of your covariates are much good. Partial p-values should tell about the same story. Check out Burnham and Anderson (2002. Model selection and multiomodel inference) or just start with David Anderson's website and pdf files there. Note: there is a school of thought that says you may use all useful stats available and not just AIC all the time. good luck. Shawn H


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