Prof Brian Ripley wrote:
>> Christoph Scherber <Christoph.Scherber <at> agr.uni-goettingen.de>
>> writes:
>>
>>>
>>> Dear all,
>>>
>>> Is there a way to retrieve standard errors from nls models?
>>> The help page tells me that arguments
>>> such as se.fit are ignored...
>>>
>>> Many thanks and best wishes
>>> Christoph
> In general using the delta method (which is I guess what you mean, local
> linearization via derivatives) is nowhere near accurate enough to be
> useful. That's why it has not been done on several occasions in the past.
> If you think it might be, see ?delta.method in package alr3.
>
> I would suggest using simulation/bootsrapping to explore the uncertainty.
> There is an example in MASS of doing so (and that illustrates some of
> the pitfalls).
Hmmm. By an example, do you mean an example of using bootstrapping to
explore uncertainty in general, or of using bootstrapping to get
standard errors of predictions from nonlinear regressions? I looked
through my copy of MASS (4th ed.) and found only section 5.7
(bootstrapping in general) and chapter 8 (nonlinear and smooth
regression, esp. p. 225 "bootstrapping" for getting bootstrap c.i.'s on
parameter estimates). I didn't find anything *specifically* covering
s.e./c.i. for nls predictions, but maybe that's not what you meant.
And yes, I meant "delta method" rather than "delta function" in my
original post. Oops.
I might add something quick/dirty/naive to the wiki giving
some examples of delta method/bootstrap approaches ...
If there is no intention to add confidence interval calculation
to predict.se in the foreseeable future might I suggest that the details
under "Value" as to what "se.fit" will do when it is implemented be
removed? (And perhaps even a statement to the effect [as you say
above] that delta method is considered unreliable?) As written it's a
bit of a tease ...
cheers
Ben Bolker
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