R help - Feb 2010 - comparison of parameters for nonlinear regression

Hi,

I have two series of data set (it's measurment of growth but under two 
different conditions).
To model these data I use the same function which is :

formula <- y ~ Asym_inf  + Asym_sup * ( (1 / (1 + (n1 * (exp( (tmid1-x) 
/ scal1) )^(1/n1) ) ) ) - (1 / (1 + (n2 * (exp( (tmid2-x) / scal2) 
)^(1/n2) ) ) ) )

After the estimation of the parameters thanks to "nls", I have 2
models.
The idea is I want to compare this 2 models and particularly I want to 
know if the parameters are different are not beteween this 2 models. Is 
there a way to do it simply in R?

Thanks in advance

-- 
Nathalie YAUSCHEW-RAGUENES
Ph.D Student

Unit? de Recherches Ecologie Fonctionnelle et Physique de l'Environnement
(EPHYSE)
INRA, Centre de Bordeaux - Aquitaine
71 Av Edouard Bourlaux
33883 Villenave d'Ornon Cedex
France

T?l : +33 (0)5 57 12 24 31
Fax : +33 (0)5 57 12 24 20
e-mail : nathalie.yauschew-Raguenes at bordeaux.inra.fr

Nathalie Yauschew-Raguenes wrote:
> Hi,
> 
> I have two series of data set (it's measurment of growth but under two 
> different conditions).
> To model these data I use the same function which is :
> 
> formula <- y ~ Asym_inf  + Asym_sup * ( (1 / (1 + (n1 * (exp( (tmid1-x) 
> / scal1) )^(1/n1) ) ) ) - (1 / (1 + (n2 * (exp( (tmid2-x) / scal2) 
> )^(1/n2) ) ) ) )
> 
> After the estimation of the parameters thanks to "nls", I have 2
models.
> The idea is I want to compare this 2 models and particularly I want to 
> know if the parameters are different are not beteween this 2 models. Is 
> there a way to do it simply in R?
> 
This depends on how much data you have and what assumptions you're
willing to make. I would probably combine the data into a single
data frame with an indicator variable for "condition". Then I would
assume equal error structure for both conditions and formulate two
nested models and thereafter use anova(model.1,model.2) to compare
the models.

But don't get too hung up on the P-value; the extra-sum-of-squares
F-test is approximate.

Have a look at the examples in ?Leaves in the Doug Bates' NRAIA
package or, even better, check out the book by Bates and Watts.

As always, the most important questions are 1) why do you want
to compare models and 2) what will you do with the result of
the comparison.

  -Peter Ehlers
> Thanks in advance
>