proportional odds logistic regression with non-negative constraint for several coefficients

library(ordinal)
data(wine, package="ordinal")
## Fit model for reference:
fm1 <- clm(rating ~ temp + contact, data=wine)
# summary(fm1)
coef(fm1)

## Now fit the same model by manually optimizing a clm-environment:
env <- clm(rating ~ temp + contact, data=wine, doFit=FALSE)
# ls.str(env) ## view contents of env
## Define negative log-likelihood function:
nll <- function(par, envir) {

## optimize with nlminb:
nlminb(start=env$par, objective=nll, envir=env)$par

# Now optimize under parameter constraints:
nlminb(start=env$par, objective=nll, envir=env,

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

        [[alternative HTML version deleted]]