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
email: [EMAIL PROTECTED]
phone: +1-510-502-5205
office room: 449 Evans Hall
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