Welcome to the wonderful world Of nonlinear model fitting in ill-conditioned 
problems. See also  chaos theory.

Bert

Sent from my iPhone -- please excuse typos.

On Apr 3, 2012, at 3:03 PM, Ramiro Barrantes <[email protected]> 
wrote:

> Hello,
> 
> I am using CentOS Linux 6.0, R 2.14.1 and nlme 3.1-103
> 
> I am trying to fit some models using nlme, and what was happening was that it 
> would get to some datasets and just "stall", the memory usage would grow and 
> grow and eventually crash.
> 
> I looked carefully into one of the datasets and ran it separately. It worked. 
>  After narrowing down on all possibilities, I found that the main difference 
> between both was in the initial values. If one subtracts them:
> 
>> initialValuesOriginal - initialValuesThatWorks
>          A.R           A.a           A.y           B.R           B.a
> -3.194922e-08  0.000000e+00  0.000000e+00 -9.249630e-08  0.000000e+00
>          B.y           C.R           C.a           C.y           D.R
> 0.000000e+00 -1.713935e-06 -8.639821e-09  1.032083e-09  3.716880e-08
>          D.a           D.y           E.R           E.a           E.y
> 0.000000e+00  0.000000e+00  1.766460e-06  0.000000e+00  0.000000e+00
> 
> How come such a small difference can have such a big effect?  (working ok, 
> versus stalling the entire computer)
> 
> I also just played with rounding to 10 decimal digits and it worked fine.
> 
> I will look into what nlme is doing tomorrow, but was wondering if perhaps 
> there is something that I don't know about how R works, or using numbers with 
> many decimals in nlme for initial values, that would create such a phenomenon.
> 
> Thanks in advance,
> Ramiro
> 
>    [[alternative HTML version deleted]]
> 
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