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