I am analyzing disease data on individual trees. The dependent variable is a disease ranking (1-4; 1 being best, 4 being worst). The a priori model is complex and includes tree size at planting (size), breeding generation (generation), genetic family nested within generation (family; different families exist within each generation), and year (yr). Year is a repeated measure where data were taken at 0.5 year intervals for 5.5 years for each tree. We had a resolvable incomplete block design, so replication (rep) and block(rep) are random effects. I am not sure how to incorporate the repeated measures into the model considering I have other random effects. I know how to do with this with Proc Mixed, but my data distribution is multinomial, requiring I use Glimmix. Should I have two random statements? I also would like to make sure the default link function is what is most appropriate for the data. I would like to make multiple comparisons and interpret differences among levels of each treatments. Here is what I have so far: proc glimmix data=blight14 method=laplace; ;
class size generation family rep block yr tree;
model maxrank= size|generation|family(generation)|yr /dist=multi cl oddsratio (diff=all label);
random yr rep block(rep) /subject=tree type=ar(1); run; Thanks for any help!
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