[R] How robust is mle in R?

[Spencer Graves]

	  R does not have one "mle routine".  Many statistical procedures do 
maximum likelihood estimation (mle) either by default or as an option. 
The "robustness" would depend on the likelihood and what you want to do 
and what you mean by "robustness.  Read the help files and check 
"www.r-project.org" -> search -> "R site search" for functions you might 
want to use.

	  S (of which R is an implementation) is a object-oriented language for 
statistics.  If you want to do standard analyses, Stata and other 
"statistical packages" may be easier to use.  If you application(s) 
involve a substantial amount of custom scripting, I know of nothing that 
beats R.  Many new statistical procedures are developed first in R and 
only later ported to other languages.  I expect this to be even more 
true in the future than it is today.

hope this helps.  spencer graves

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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