[R] Using optim with parameters that are factors (instead of continuous parameters)

[Lucas Merrill Brown]

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