On 5/10/07, Frank E Harrell Jr <[EMAIL PROTECTED]> wrote: > Paul Johnson wrote: > > This is a follow up to the message I posted 3 days ago about how to > > estimate mixed ordinal logit models. I hope you don't mind that I am > > just pasting in the code and comments from an R file for your > > feedback. Actual estimates are at the end of the post. > > . . . > > Paul, > > lrm does not give an incorrect sign on the intercepts. Just look at how > it states the model in terms of Prob(Y>=j) so that its coefficients are > consistent with the way people state binary models. > > I'm not clear on your generation of simulated data. I specify the > population logit, anti-logit that, and generate binary responses with > those probabilities. I don't use rlogis.
Thank you. I don't think I'm telling you anything you don't already know, but for the record, here goes. I think the difference in signs is just convention within fields. In choice models (the econometric tradition), we usually write that the response is in a higher category if eta + random > cutpoint and that's how I created the data--rlogis supplies the random noise. Then eta - cutpoint > random or cutpoint - eta < random and so Prob ( higher outcome ) = Prob ( random > cutpoint - eta) In the docs on polr from MASS, V&R say they have the logit equal to cutpoint - eta so their parameterization is consistent with mine. On the other hand, your approach is to say the response is in a higher category if intercept + eta > random, where I think your intercept is -cutpoint. So the signs in your results are reversed. -cutpoint + eta > random But this is aside from the major question I am asking. Do we think that the augmented data frame approach described in Cole, Allison, and Ananth is a good alternative to maximum likelihood estimation of ordinal logit models, whether they are interpreted as proportional odds, continuation, or stopping models? In the cases I've tested, the parameter estimates from the augmented data frame are consistent with polr or lrm, but the standard errors and other diagnostic informations are quite different. I do not think I can follow your suggestion to use penalties in lrm because I have to allow for the possibilities that there are random effects across clusters of observations, possibly including random slope effects, but certainly including random intercepts for 2 levels of groupings (in the HLM sense of these things). Meanwhile, I'm studying how to use optim and numeric integration to see if the results are comparable. pj > See if using the PO model with lrm with penalization on the factor does > what you need. > > lrm is not set up to omit an intercept with the -1 notation. > > My book goes into details about the continuation ratio model. > > Frank Harrell > -- Paul E. Johnson Professor, Political Science 1541 Lilac Lane, Room 504 University of Kansas ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.