# odinal regression

I get an output of ordinal regression model using r. it has 3 levels but I can't understand how to interpret can you explain to me what is the meaning of 0/1 and 0/2?

here logic is the predicted variable....

                               Value Std. Error     t value      p value
sex                           -0.1713340  0.6552480  -0.2614796 7.937227e-01
nationality                    1.9440132  1.5624353   1.2442200 2.134186e-01
race                         -17.6845396  1.3415344 -13.1823233 1.109215e-39
religion                       6.4993120  0.7103028   9.1500582 5.690286e-20
place.of.origin               -0.1388703  0.5199239  -0.2670974 7.893942e-01
recidence.at.university.time  -0.3090342  1.2186897  -0.2535791 7.998208e-01
0|1                           -9.2347103  2.2702138  -4.0677713 4.746494e-05
1|2                           -7.8270152  2.3122740  -3.3849861 7.118186e-04


• You need to investigate why race has a coefficient which is tending to $-\infty$. Check this site for separation. – mdewey Feb 16 at 16:43

Not sure what library you're using but I would assume $$0|1$$ to be the value of the constant term $$\beta_{0[i=1]}$$ for equation:
$$log~{Odds_{1|0}} = \beta_{0[i=1]} +\beta_{1}x_1 +\beta_{2}x_2 + ... +\beta_{n}x_n$$
and $$1|2$$ to be the value of the constant term $$\beta_{0[i=2]}$$ for equation:
$$log~{Odds_{2|1}} = \beta_{0[i=2]} +\beta_{1}x_1 +\beta_{2}x_2 + ... +\beta_{n}x_n$$
Due to the proportional odds assumption, $$\beta_1, \beta_2, ..., \beta_n$$ should be equal across all equations, that's why you only get one estimate for each of them. The constant term $$\beta_{0[i]}$$, however, will be different for ever base level $$i$$ of the ordinal response variable, and therefore has a unique estimate in every equation.