Re: [R] nls: different results if applied to normal or linearized data

From: Gabor Grothendieck <ggrothendieck_at_gmail.com>
Date: Wed, 05 Mar 2008 09:06:14 -0500

Write out the objective functions that they are minimizing and it will be clear they are different so you can't expect the same results.

On Wed, Mar 5, 2008 at 8:53 AM, Wolfgang Waser <wolfgang.waser_at_utu.fi> wrote:
> Dear all,
>
> I did a non-linear least square model fit
>
> y ~ a * x^b
>
> (a) > nls(y ~ a * x^b, start=list(a=1,b=1))
>
> to obtain the coefficients a & b.
>
> I did the same with the linearized formula, including a linear model
>
> log(y) ~ log(a) + b * log(x)
>
> (b) > nls(log10(y) ~ log10(a) + b*log10(x), start=list(a=1,b=1))
> (c) > lm(log10(y) ~ log10(x))
>
> I expected coefficient b to be identical for all three cases. Hoever, using my
> dataset, coefficient b was:
> (a) 0.912
> (b) 0.9794
> (c) 0.9794
>
> Coefficient a also varied between option (a) and (b), 107.2 and 94.7,
> respectively.
>
> Is this supposed to happen? Which is the correct coefficient b?

>
>
> Regards,
>
> Wolfgang
>
> --
> Laboratory of Animal Physiology
> Department of Biology
> University of Turku
> FIN-20014 Turku
> Finland
>
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>



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https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Wed 05 Mar 2008 - 14:21:04 GMT

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