Jay P Sah wrote:
> Recent discussion on how to compare regression lines was really =
> enlightening. As far as I understood, the discussion was about comparing =
> slopes in linear regressions.=20
>
> =20
>
> I have similar problems in comparing non-linear regression lines. Here, =
> it goes what I want to compare. I have shrub biomass and pine seedling =
> density data collected in 160 50-m^2 plots. I used non-linear regression =
> (y =3D b0*(1-exp(-b1^x)) to model the cumulative seedling density =
> against shrub biomass. I am interested to test if this regression curve =
> (observed pattern) differs from another similar curve (a reference =
> model), generated by using Poisson cumulative distribution function. My =
> questions are: how to compare these two non-linear regression lines or =
> the coefficients there in? Are the methods described in Zar's book and =
> elsewhere, more commonly used for comparing slopes in linear regression, =
> also applicable for non-linear regression lines? =20
>
>   
If the two models are nested, then you can compare them using an F test 
(I'm not sure if the results are exact, but they should be OK with a 
decent amount of data).  If they're not nested, but have the same number 
of parameters, then you can compare the residual sums of squares: the 
smallest wins!  If they have different numbers of parameters and are not 
nested, then there are several ways of comparing them, for example, 
using AIC, BIC etc.

As a starter, I would recommend plotting the residuals against the 
predicted values: it may be that it's obvious that one curve does not 
fit.  Or indeed that neither curve fits!

Incidentally, I'm not sure what you mean by using the Poisson cdf: 
that's a stepped function, but it sounds like your data are continuous.

Bob

-- 
Bob O'Hara
Department of Mathematics and Statistics
P.O. Box 68 (Gustaf Hällströmin katu 2b)
FIN-00014 University of Helsinki
Finland

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