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

From: Wolfgang Waser <wolfgang.waser_at_utu.fi>
Date: Wed, 05 Mar 2008 15:53:27 +0200

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?



Laboratory of Animal Physiology
Department of Biology
University of Turku
FIN-20014 Turku

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Received on Wed 05 Mar 2008 - 14:04:20 GMT

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