Ajay Narottam Shah wrote:
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?
give optim() a function to optimize which do not depend on those
parameters ...
* 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?
Kjetil
--
Kjetil Halvorsen.
Peace is the most effective weapon of mass construction.
-- Mahdi Elmandjra
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