Hello, I am stuck with tihs statistic analysis, and I hope you can help me to solve this problem. I want to analyze the proability of being alive at the end of an experiment (response variable is mort3, a binary data, 0=dead ,1=alive). I have 30 individuals (geno) from3 diffrent populations (pop), so geno is nested in pop. Since the samples are algae, I split the individuals into different clones (clone) and I exposed half of them to one treatment, and the other half to an other (trm can be future or current conditions). pop, geno are random factors, and trm fixed. my dataset looks like this: pop geno clone trm mort3 pop1 1 1 Future 0 pop1 1 2 current 0 ... pop2 1 1 current 1 I run already this glimmix proc: proc glimmix data=WORK.sasdatasetfv method=laplace; class trm pop geno; model mort3 (descending) = trm/ distribution=binary; random pop geno(pop) trm*pop trm*geno(pop) ; lsmeans trm /cl ilink; covtest'pop' 0 . . . ; covtest'genpop' . 0 . .; covtest'trmpop' . . 0 .; covtest'trmgenopop' . . . 0 ; covtest'total' zerog; run; From my point of view that isn´t correct because clone should be considered as repeated measure of each single geno, so I tried to use this: proc glimmix data=centr; class geno pop trm clone; model mort3= trm / distribution=binary; random clone/subject=geno; random trm/ subject=geno*clone; random geno/ subject=pop; random trm/ subject=pop*geno; lsmeans trm/ ilink; covtest'geno' 0 . . .; covtest 'genotrm' . 0 . .; covtest 'pop' . . 0 .; covtest 'trm*pop' . . . 0; run; unfortunately, this runs for many hours, so that I have to stop it. Do you think is correct in this way? Do you have any suggestion about? thansk in advance cheers Luca
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