Re: [R] nls() vs lm() estimates

From: Janne Huttunen <>
Date: Fri, 13 Jun 2008 11:21:52 -0700

Héctor Villalobos wrote:
> Hi,
> I'm trying to understand why the coefficients "a" and "b" for the model: W = a*L^b estimated
> via nls() differs from those obtained for the log transformed model: log(W) = log(a) + b*log(L)
> estimated via lm(). Also, if I didn't make a mistake, R-squared suggests a "better" adjustment
> for the model using coefficients estimated by lm() . Perhaps I'm doing something wrong in
> nls()?

I didn't tried your code, but in general these estimates are different: for the former estimate you minimize the norm of the difference W-a*L^b (W are ) and for the latter you minimize the norm of the difference log(W)-(log(a)+b*log(L)). The solution for these problems are equal. That which approach you should choose depends on errors, for additive error model the former is better choice.

Janne Huttunen
University of California
Department of Statistics
367 Evans Hall Berlekey, CA 94720-3860
phone: +1-510-502-5205
office room: 449 Evans Hall

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Received on Fri 13 Jun 2008 - 21:09:12 GMT

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