Ben, Thank you for the incredibly helpful suggestions and links. I've been exploring each over the past few days, and for anyone else's future reference, here's what I've found.
(1) I was able to use SANN to specify how to choose new candidate solutions, but I wasn't able to easily use SANN for a model that includes both discrete and continuous parameters. That would require designating two separate rules for choosing new candidate solutions -- one rule for the continuous parameters and one rule for the discrete parameters. (2) Your second suggestion ended up solving the problem best for the needs of this data. I wrote a continuous function that looks a lot like a discrete pulse, and optim was able to find its way towards the specification with the maximum likelihood. A function of the general form f(x) = 1/(k + (c - x)^n) does the trick, where c represents the location of the discrete jump. I then optimized over potential values of c. (3) Generating log-likelihoods for each separate value of the parameter works well, especially for a parameter with few potential values. Since I'm also running a specification with individual-specific thresholds, however, re-running the regression five times for each individual is a little unwieldy. So it made the most sense to use solution #2. Thanks again for your prompt and productive response! Lucas ______________________________________________ R-help@r-project.org 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.
