Hello, I am currently running a proc genmod with poisson distribution on a dataset and was looking for LSmeans estimated for one of the variables. When I do this without an offset included and I ask to transform the LSmeans back to response scale (ilink) I get estimates that make intuitive sense (on the correct scale of the original count variable). However when I include an offset in the model (based on the log of another variable), the LSmeans seem to be presented on a different scale, even with the ilink option included. Results are strongly correlated though. So I was wondering what exactly is happening in the LSmeans estimation when an offset is included in the model. And additionaly how to get the LSmeans back to response scale with an offset included in the model. Ideally I want to get estimates on the original count scale with an offset included in the model. model with offset: ods output LSmeans=LSmeans; proc genmod data=control; class F2 block ; model eggs = F2 block / type3 dist=poisson offset=l_area; lsmeans F2/cl ilink;run;quit; model without offset: ods output LSmeans=LSmeans1; proc genmod data=control; class F2 block ; model eggs = F2 block / type3 dist=poisson ; lsmeans F2/cl ilink;run;quit; The first model produces LSmeans with the following values for the "mean" column in the LSmeans output: 0.04437 0.07704 0.03344 0.03425 0.02408 0.05762 The second model produces LSmeans with the following values for the "mean" column in the LSmeans output: 9.2543 15.1355 9.9462 6.5443 4.8722 10.4939 Thank you in advance.
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