[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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