I learned R & MLE in the last few days. It is great! I wrote up my explorations as
http://www.mayin.org/ajayshah/KB/R/mle/mle.html I will be most happy if R gurus will look at this and comment on how it can be improved. I have a few specific questions: * Should one use optim() or should one use stats4::mle()? I felt that mle() wasn't adding much value compared with optim, and in addition, I wasn't able to marry my likelihood functions to it. * One very nice feature of mle() is that you can specify a few parameters which should be fixed in the estimation. How can one persuade optim() to behave like that? * Can one use deriv() and friends to get analytical derivatives of these likelihood functions? I found I wasn't able to make headway when I was using vector/matrix notation. I think the greatness of R lies in a lovely vector/matrix notation, and it seems like a shame to have to not use that when trying to do deriv(). * For iid problems, the computation of the likelihood function and it's gradient vector are inherently parallelisable. How would one go about doing this within R? -- Ajay Shah Consultant [EMAIL PROTECTED] Department of Economic Affairs http://www.mayin.org/ajayshah Ministry of Finance, New Delhi ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
