@GavinB wrote:
Example of a code that I would run:
ods graphics /imagemap=on;
proc mixed data=performance2 plots=all;
class TT time box block;
model DWG=TT|time/ ddfm=kenwardroger s cl influence(est effect=box iter=5) outp=mixout;
repeated time /subject=box type=un;
random INT/subject=block s cl;
store mixmodel;
run;
proc plm restore=mixmodel;
effectplot interaction(x=time sliceby=TT) / ilink clm connect;
slice TT*time/ sliceby=time pdiff=all cl ilink means plots=diffogram lines adjdfe=row adjust=simulate;
lsmeans TT/ pdiff=all adjust=simulate means lines;
run;
My time would be the timepoints at which I have measured the bodyweight. If I run this piece of code with bodyweight the lsmeans are quite close to the actual means. If I run it with daily weight gain (as I said before, I calculate this value by substracting previous BW with new BW and dividing this by the # of days) the lsmeans of the entire period (Not the individual timepoints) are quite far off...... Example of the dataset:
We don't have your data, so we can't run your code; please provide a representative portion of your data that illustrates the problem following these instructions (and not via an attached file). https://communities.sas.com/t5/SAS-Communities-Library/How-to-create-a-data-step-version-of-your-data-AKA-generate/ta-p/258712
You also haven't shown us the allegedly incorrect output and correct output, which I think is required for us to understand the issue. And you are making an incorrect assumption: "the lsmeans are quite close to the actual means", which does not have to be true in all situations, and depends on a lot of things, including the presence of "balanced data". Is there something else allegedly incorrect, or is the only thing that is incorrect that the lsmeans are not close to the actual means?
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