[R] problem with nls starting values

[Bert Gunter]

Good point, Ben.

I followed up my earlier reply offline with a brief note to Benedikt
pointing out that "No" was the wrong answer: "maybe, maybe not" would
have been better.

Nevertheless, the important point here is that even if you do get
convergence, the over-parameterization means that the estimators don't
mean anything: they are poorly determined/imprecise. This is a
tautology, of course, but it is an important one. My experience is, as
here, the poster wants to fit the over-parameterized model because
"theory" demands it. That is, he wants to interpret the parameters
mechanistically. But the message if the data is: "Sorry about that
guys. Your theory may be fine, but the data do not contain the
information to tell you what the parameters are in any useful way."
We gloss over this distinction at our peril, as well as that of the
science.

Cheers,
Bert

On Thu, Sep 27, 2012 at 2:17 PM, Ben Bolker <bbolker at gmail.com> wrote:
> Bert Gunter <gunter.berton <at> gene.com> writes:
>
>>
>> On Thu, Sep 27, 2012 at 12:43 PM, Benedikt Gehr
>> <benedikt.gehr <at> ieu.uzh.ch> wrote:
>> > now I feel very silly! I swear I was trying this for a long time and it
>> > didn't work. Now that I closed R and restarted it it works also on my
>> > machine.
>> >
>> > So is the only problem that my model is overparametrized with the data I
>> > have?
>> Probably.
>>
>> > however shouldn't it be possible to fit an nls to these data?
>> (Obviously) no.
>>
>> I suggest you do a little reading up on optimization.
>> Over-parameterization creates high dimensional ridges.
>
>   However, I will also point out that (from my experience and
> others') nls is not the most robust optimizer ... you might consider
> nlsLM (in the minpack.lm package), nls2 package, and/or doing nonlinear
> least-squares by brute force using bbmle::mle2 as a convenient wrapper
> for optim() or optimx().
>
>   cheers
>     Ben Bolker
>
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-- 

Bert Gunter
Genentech Nonclinical Biostatistics

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