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Re: nonlinear regression

To: "Spencer Graves" <spencer.graves@pdf.com>
Subject: Re: nonlinear regression
From: "A.J. Rossini" <blindglobe@gmail.com>
Date: Sun, 3 Sep 2006 20:00:12 +0200
Cc: Paternostro.Amy@epamail.epa.gov, s-news@lists.biostat.wustl.edu
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References: <OFF29EFAE3.541A6C3C-ON852571D4.004B40A5-852571D4.004BB435@epamail.epa.gov> <44FA2DFC.3040100@pdf.com> <1abe3fa90609022316ve8d59bdnff936eb476b65dd2@mail.gmail.com> <44FB16C5.4040809@pdf.com>
I'm not Andy.   And I disagree -- it might provide an equally sensible
objective function.  log-normal and poisson can be similar for large
means, which can be faked via scaling, depending on the data and the
problem.

Of course, it all depends, as usual.

On 9/3/06, Spencer Graves <spencer.graves@pdf.com> wrote:
Hi, Andy:

      Correct.  However, it should at least get her a more sensible
objective function.

      Spencer Graves

A.J. Rossini wrote:
> But that wouldn't necessarily do nonlinear regression.
>
> On 9/3/06, Spencer Graves <spencer.graves@pdf.com> wrote:
>>       Have you considered 'glm(... family=poisson)'?
>>
>>       Hope this helps.
>>       Spencer Graves
>>
>> Paternostro.Amy@epamail.epa.gov wrote:
>> > I am currently running a script in S-PLUS 2000 on a Windows platform
>> > using the canned S-PLUS nonlinear regression function nls. I was
>> > wondering if anyone on the list has had any experience creating their
>> > own nonlinear regression function using something other than least
>> > squares? I am particularly interested in utilizing a Poisson family in
>> > my calculations.
>> >
>> > Thanks,
>> > Amy Paternostro
>> >
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>



--
best,
-tony

blindglobe@gmail.com
Muttenz, Switzerland.
"Commit early,commit often, and commit in a repository from which we can easily
roll-back your mistakes" (AJR, 4Jan05).

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