polychotomous response data
Daniel Ezra Johnson <danielezrajohnson at ...> writes:
Can anyone suggest what I might do to analyze data where: - response has more than two values (nominal) - observations are grouped by subject - predictors are both categorical and continuous - some predictors are within-subject, some are between-subject If it wasn't for the within-subject predictors, I thought this was going to be a compositional data analysis problem, so I was looking into the "compositions" library (which I found confusing).
Nope, compositions can be used when you measure the composition of a particular things, say you have a soil and you measure % os sand, % of, ...
However, treating each subject's data as a single composition, while it makes sense, is not going to enable the analysis of within-subject predictors. Is there a way to use lme4 or another library to extend GLMM to a multi-category response?
I would first fit a "threshold" model with polr() in MASS. Then I would try to add "mixed" effects with use of BUGS. Latest Book by Gelman and Hill is a very good resourse about this topic.