Dear all,
I'm modeling growth curve of some ecosystems with respect to their
rainfall-productivity relationship using a simple linear regression
(ANPP(t)=a+b*Rain(t)) and a modified version of the Brody Model
ANPP(t)=a*(1-exp(-b*rain(t)))
I would like to know why the "best model" is function of the criteria that I
use (maximizing the fit using R2 or testing the Null hypothesis with BIC/AIC).
To compute the R2, I used the following formula r2=mss/(mss+rss) where
mss=sum((fitted(model)-mean(fitted(model)))^2) and rss=sum(resid(model)^2)
I think that the R2 is good enough for the model selection knowing the
candidate models both have two parameters (so no to care about the principle of
parsimony) and my guess is that the models needs to have the same form (which
is not the case here: linear form vs exponential form) ) or nested to be
compared with frequentist or Bayesian approaches such as the AIC and BIC
criterion .
Thank you very much in advance
Armel
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