[R] How robust is mle in R?
Roger D. Peng
rpeng at stat.ucla.edu
Sun Jul 13 22:38:28 CEST 2003
There's no 'mle' routine in R. For doing general maximum likelihood
estimation I often use 'optim' or 'nlm'. I find 'optim' to be very
useful, although you have to become familiar with all of the
options/arguments in order to use it successfully. For example, you
often have to provide scaling information (via 'parscale') to 'optim' in
order for a good solution to be found.
If you have a very complicated likelihood surface, than finding a good
solution will likely be the exception rather than the rule, and I think
that's independent of what software you use. R's 'optim' function
provides four different procedures for optimizing a function, each of
which has its advantages and disadvantages. I suggest checking out the
help page for 'optim', which is very detailed.
Peter Muhlberger wrote:
>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?
>R-help at stat.math.ethz.ch mailing list
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