Table 1. Comparisons of outcome categories for each equation in ordinal logistic regression model.

Equations	Pooled categories	Versus	Pooled categories	R code for computing probability
1	0		1, 2, 3	plogis(x)
2	0, 1		2, 3	plogis(x-mod.ord$coef[1]+mod.ord$coef[2])
3	0, 1, 2		3	plogis(x-mod.ord$coef[1]+mod.ord$coef[3])

Each equation models the odds of being in the set of categories on the left versus the set of categories on the right. x is the linear predictor of the ordinal regression model.