Re: [R] Question concerning mle

From: Rainer M Krug <>
Date: Mon 19 Jun 2006 - 17:53:53 EST

Thanks a lot Spencer for your tips - I'll look into all of them.


Spencer Graves wrote:
> It's difficult to say much at this level of generality, but I have
> four suggestions:
> 1. Have you tried creating a reasonable grid of starting values
> using "expand.grid" and then plotting the resulting likelihood surface?
> If you have more than 2 parameters, you may want to use 'lattice'
> graphics. This should tell you if the functions seems unimodal, convex,
> etc., in the region you covered and at the resolution of your grid.
> 2. Have you tried method="SANN" = simulated annealing? I might
> try one pass with SANN, then refine the solution found by SANN using BFGS.
> 3. After you have a solution, you can then try profile likelihod.
> Unfortunately, my experience with profile.mle has been mixed. I
> actually made local copies of mle and profile.mle and found and fixed
> some of the deficiencies of each. I didn't test them enough to offer
> the results to the R Core Team, however.
> 4. Have you looked at Venables and Ripley (2002) Modern Applied
> Statistics with S, 4th ed. (Springer)? It's a great book for many
> things, including the use of expand.grid and 'optim'.
> hope this helps.
> Spencer Graves
> Rainer M Krug wrote:

>> Hi
>> I hope this is the right forum - if not, point me please to a better one.
>> I am using R 2.3.0 on Linux, SuSE 10.
>> I have a question concerning mle (method="BFGS").
>> I have a few models which I am fitting to existing data points. I
>> realised, that the likelihood is quite sensitive to the start values for
>> one parameter.
>> I am wondering: what is the best approach to identify the right initial
>> values? Do I have to do it recursively, and if yes, how can I automate
>> it? Or do I have to play with the system?
>> I am quite confident that the resulting parameters are the optimal for
>> my problem - but can I verify it?
>> Thanks,
>> Rainer

Rainer M. Krug, Dipl. Phys. (Germany), MSc Conservation
Biology (UCT)

Department of Conservation Ecology and Entomology
University of Stellenbosch
Matieland 7602
South Africa

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