Hi,
What is the best way to communicate to non-statisticians the Goodness
of Fit (gof) of an ordered logit/ordered probit model?
For OLS, there is the trusty R2, letting you tell a non-statistician,
"I can explain X% of the variation in the dependent variable."
For logit/probit, I've used the probability of correct classification,
type I and type II error rates as my go-to metric for gof.
Is there a corresponding metric for ordered logit/ordered probit?
I've read about psuedo R2 and it's faults. Probability of correct
classification doesn't seem fair given the multiple categories of the
dependent variable - if my model predicts you'll be a 2 but you're a
3, I get no credit for being close.
Please feel free to just reply with a link/manual reference that I
should read and I'll do the reading.
Thanks in advance,
-Dan
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