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!
yes, try adding an intercept keyword effect to the first random statement and moving the rep and block(rep) into a second random statement with subject=tree and type=vc, fa0(1), fa0(2) or chol options.
Also adding ddfm=kr2 to the model statement options might help.
I'm not sure if maxrank needs to be specified as a class statement - does it work as expected without?
Thanks! This helps. max rank should not be a class statement.
Are you ready for the spotlight? We're accepting content ideas for SAS Innovate 2025 to be held May 6-9 in Orlando, FL. The call is open until September 25. Read more here about why you should contribute and what is in it for you!
Learn how use the CAT functions in SAS to join values from multiple variables into a single value.
Find more tutorials on the SAS Users YouTube channel.