Re: [R] How robust is mle in R?

[Roger D. Peng]

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. 

-roger

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?
Thanks,
Peter
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