I'm afraid that the GLIMMIX procedure does not have much facility for testing the proportional odds assumption, if that is what you are after. About the best that you could do would be to construct all binary splits on your response variable and fit the GLIMMIX code which you have to each candidate binary split. You could then construct graphs in which a predictor variable is plotted on the X axis and the Y axis is the estimated logit. For each binary split, you would plot the resulting logits for each level of a categorical predictor variable or for two values of a continuous predictor variable. (For a continuous predictor variable, these can be any two values that you choose so long as you employ the same two values for every binary split.) Further detail about construction of these graphics is given below.
In your case, suppose that score has four ordered levels with values 1, 2, 3, and 4. You would create three binary splits of the data: 1 vs >1, 2 vs >2, and 3 vs >3. Construct three new variables:
score1 = (score>1);
score2 = (score>2);
score3 = (score>3);
and fit your glimmix code to each of these three new response variables. I would add the LSMEANS statements
lsmeans genotype / at sweek=;
lsmeans genotype / at sweek=;
I presume that genotype has three levels. Let's suppose that those three levels are AA, AT, and TT. From these LSMEANS statements, you can construct a data set as shown below:
Response LSM stmt Genotype LSM value
score1 1 AA
score1 1 AT
score1 1 TT
score2 1 AA
score2 1 AT
score2 1 TT
score3 1 AA
score3 1 AT
score3 1 TT
score1 2 AA
score1 2 AT
score1 2 TT
score2 2 AA
score2 2 AT
score2 2 TT
score3 2 AA
score3 2 AT
score3 2 TT
For the LSM statement 1, create a graphic which has an X-axis with values AA, AT, and TT. Plot the score1 LSM values for AA, AT, and TT with a line joining those three values. Do the same for score2 and score3 LSM values (using different linetypes or line colors so that you can distinguish the logit traces from each model.) You will have three lines which should be approximately parallel if the proportional odds assumption is true. You will want to do the same for LSM statement 2, creating a separate graph for that set of results.
You can also condition on a genotype and use the LSM statement (representing your time effect) as the X-axis variable. That is, select the six LSM values with genotype AA and plot the logits against time (LSM statement) for the pair of score1 values, joining these with a line. Do the same for score2 and score3 values. You will generate a plot for genotype AA, another plot for genotype AT, and a third for genotype TT. Nonparallel lines in any of these plots indicates violation of the proportional odds assumption.
Of course, one does not expect exact parallel lines, so some judgement comes into play here.
If you can do away with METHOD=LAPLACE for approximating the integration of the random effects, then you could do the following for a more formal test:
data new_x;
set x;
record=_n_;
new_score = (score>1); logit_spec=1; output;
new_score = (score>2); logit_spec=2; output;
new_score = (score>3); logit_spec=3; output;
run;
proc glimmix data=x;
class logit_spec farm genotype animal;
model new_score = logit_spec genotype sweek genotype*sweek
logit_spec*genotype logit_spec*sweek logit_spec*genotype*sweek
/ dist=binary link=glogit s oddsratio (diff=all);
random int / subject=farm s;
random int sweek / subject=animal s;
random logit_spec / subject=record residual type=un;
run;
If the interactions of logit_spec with the other predictors are significant, that suggests that the parallel lines assumption is not valid. But this might not be an easy model to fit. Moreover, tests of this type are known to be liberal, so that you might declare that the lines are not parallel too often.
