# Proportional odds logistic regression with nominal (unordered) categories

Suppose that you've got a logistic regression with multiple nominal outcomes that cannot be ordered in a theoretically meaningful way. Assume further, however, that the proportional odds assumption nonetheless holds true for these categories for a given order. In this scenario, would it be possible to fit an ordered logistic regression model?

What I'm really asking is if there's any reason, apart from the proportional odds assumption, for why a ordered logit needs ordered categories? Is it simply a mathematical assumption or is there something else to it that I'm presently missing?

There is a blend between the prop. odds model and the polytomous logistic model called the partial proportional odds model. See http://www.citeulike.org/user/harrelfe/article/13264679. I believe the R vgam package can fit it. But it needs some kind of prespecification.
A useful exercise is checking the ordinality and proportional odds assumptions separately for each predictor as examplified in my course note handouts available as a link from http://biostat.mc.vanderbilt.edu/rms (R function plot.xmean.ordinaly).