# How to use ordinal logistics regression with random effects, ability to assess model fit, and test multiple hypotheses?

I am trying to perform an ordinal logistic regression in R and have recently attempted to use clmm2() function from the ordinal package. Unfortunately I've come to a roadblock.

1. How do I assess model fit?

2. How do I test multiple hypotheses by comparing AIC values of multiple models if I also need to assess the proportional odds assumption?

The data I'm dealing with: The ordered response variable is the consistency of bird food availability. We asked respondents to give us the percent days food was available for each season. I then bucketed it into none, pulsed, constant. So I have four rows for each respondent, making respondent a random variable in the model.

I have multiple predictors: season, age, etc.

• Have you seen this recent thread? stats.stackexchange.com/questions/461273/… The non-proportional odds models are nested within the proportional odds modes, so you can use likelihood ratio testing (anova) to compare models, assuming the predictors are the same between the two models. Apr 21, 2020 at 13:08
• @ErikRuzek I did see that thread. I had trouble getting the mixor() function to run and when I did, I didn't understand the output. I also couldn't find anything to use to assess model fit. Plus, this doesn't get at the hurdle I have -- compare hypothesis models by AIC first or assess prop-odds assumption first? Apr 21, 2020 at 18:48
• I would probably figure out the model you want to run based on testing your hypotheses using clmm2 and then use mixor to test the non-proportional odds assumption. Can you post your clmm2 and mixor code so we can help with diagnosis? Apr 21, 2020 at 19:57
• @ErikRuzek thank you for the suggestion. I've had trouble understanding the mixor output and better understand the clmm2 output. Is there a reason you suggest mixor over clmm2? I think clmm2 can be used to test the assumption as well. Apr 24, 2020 at 15:46
• It appears it is possible to test for non-proportional odds with clmm2 using the nominal = ~ predictor_name option. Then you can run a likelihood ratio test using anova to determine whether the non-proportional odds model provides a better fit to the data. It is not clear to me if you have to do this for each predictor separately (I think that is the case). In mixor you can allow for multiple predictors to have interactions with the thresholds, although estimation may take a while. Apr 24, 2020 at 19:33