Hi Steve, Thank you for your time in looking at my question. I have tried to use the model you proposed, unfortunately the model refuses to converge. I have tried increasing the number of iterations up to 200 and lowering the convergence criterion to 1e-4, also a combination ot the two does not result in a converged model. Also sorting by date, registered and ID did not result in convergence. What did work was introducing a new continues numeric variable which represents date (datenum) in the model but not the repated statement, using the following model: proc glimmix data=chicken; class date id treatment; model registered (event='1') = treatment|datenum factor1|factor2 / link=logit dist=binomial solution; random intercept/ subject=id(treatment); random date/subject= ID(treatment) residual; Lsmeans treatment/diff; run ; For modeling the R-side effect a variable has to be identified as a class variable thus is introducing a continues variable into the modelstatement as a representation a correct solution? I doubt it is because i suspect i remove the repeated nature of the data by using datenum instead of date. Regarding modelling the repeated measures using the R-side. For glimmix it was the only method i could clearly find online and i am not aware of any other methods of doing this, do you know of any other methods? In regard to you question about the dates and the autoregressive structure: I followed chickens for 26 days continouesly with not all id's being observed on the same days. If chickens were not registred the dataset automatically defaults to setting an observation of the chicken not being registred.I have attempted to work with an ar(1) autoregressivestructure before however i find that the model will often not converge. Surprisingly when i set type=ar(1) in both the model you proposed and the one depicted above the models would only converge when i set pconv=1e-4, other combinations of pconv and inititer had no effect. However, in the model you proposed factor1 and factor2 had no output while they had an output using datenum instead of date. I stumbled upon changing the convergence criterion somewhere but am uncertain what levels are stil viewed as correct in modelling? In regard to the interactions between variables, the model was indeed overspecified. I have removed the interaction between treatment and the weatherfactors but did include an interaction between them, as they respectively represent windspeed/direction and rainfall. Mariëlle
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