Repeated measures in Glimmix with incomplete block design

[StacyClark]

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!

[Damien_Mather]

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?

[StacyClark]

Thanks! This helps. max rank should not be a class statement.