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I'm running ordered probit regression models with polr in R. This question states that for prediction, polr returns the category with the highest probability for given $x$. I would have expected that the predicted category is equal to the number of estimated thresholds ($\hat{\zeta}$) exceeded by $x' \hat{\beta}$ (what imo would be in line with the model assumptions). Is there a reason, why the polr-approach is "better" then the thresholds-approach?

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