Peter, The R "optim" function is what you probably want to read up on for ML in R. It may or may not be less "complaining" than the Stata ML functions. Optim provides several alternative algorithms which may help you find one that is best for your problem. On the other hand, it sounds like you have a difficult likelihood and/or recalcitrant data. It seems likely that your likelihood and data are going to cause more problems than are due to any peculiarities of the algorithms in Stata OR R. No magic bullets in either, of course. For what it is worth, I've not managed to "break" the optim function in R.
Comparatively, I think the Stata ML functions make access to the variables and specification of the model a little easier and require fewer lines of code. Stata also then provides standard post estimation commands for the ML results. R (optim, actually) will return an object with the usual components that you can also use for any post estimation purposes. But in R you'll probably write a few more lines of code to specify the model and manipulate the returned results. You probably need to learn a bit more to program this effectively in R than you need to learn to do the same thing in Stata. Also, Stata's "ml check" provides a nice test of your code before you loose it on the data! Gauss's "maxlik" routines would be another possibility, if you have or are able to acquire Gauss. Charles /****************************************** ** Charles H. Franklin ** Professor, Political Science ** University of Wisconsin, Madison ** 1050 Bascom Mall ** Madison, WI 53706 ** 608-263-2022 Office ** 608-265-2663 Fax ** mailto:[EMAIL PROTECTED] (best) ** mailto:[EMAIL PROTECTED] (alt) ** http://www.polisci.wisc.edu/~franklin ******************************************/ -----Original Message----- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] Behalf Of Peter Muhlberger Sent: Sunday, July 13, 2003 9:55 AM To: rhelp Subject: [R] How robust is mle in R? A newbie question: I'm trying to decide whether to run a maximum likelihood estimation in R or Stata and am wondering if the R mle routine is reasonably robust. I'm fairly certain that, with this data, in Stata I would get a lot of complaints about non-concave functions and unproductive steps attempted, but would eventually have a successful ML estimate. I believe that, with the 'unproductive step' at least, Stata gets around the problem by switching to some alternative estimation method in difficult cases. Does anyone know how robust mle is in R? Thanks, Peter ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
