What I tried is just a simple binary probit model. Create a random data and use "optim" to maximize the log-likelihood function to estimate the coefficients. (e.g. u = 0.1+0.2*x + e, e is standard normal. And y = (u > 0), y indicating a binary choice variable)
If I estimate coefficient of "x", I should be able to get a value close to 0.2 if sample is large enough. Say I got 0.18. If I expand x by twice and reestimate the model, which coefficient should I get? 0.09, right? But with "optim", I got something different. When I do the same thing in both Gauss and Matlab, I can exactly get 0.09, evidencing that the coefficient estimator is reliable. But R's "optim" does not give me a reliable estimator. -- View this message in context: http://r.789695.n4.nabble.com/Poor-performance-of-Optim-tp3862229p3863969.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ 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.
