Hi,
I am working on proportional odds logistic regression, and trying to figure
out how to specify the constraint for several predictors. Those non-negative
constraints for some predictors are for practical purpose.
I have seen some one posted passing box constraint with L-BFGS-B with logistic
regression.
What I did not is to use polr() to solve the proportional odds, and modify the
source code for polr() by passing the lower bounds to the optim() and change
the method to L-BFGS-B.
Then I realized that polr() generate a start value for all coefficients with
glm.fit, which can still start from negative.
So my question is that does the start value having negative while the
optimization has a lower bound as 0.00001. Does it matter?
Or is there another way of implementation to solve proportional odds while
forcing some coefficients as non-negative.
Thanks so much!
Zhao
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