R help - Sep 2010 - Maximum log likelihood estimates of the parameters of a nonlinear model.

Dear all,

Is it possible to generate AIC or something equivalent for nonlinear
model estimated based on maximum log likelihood l in R?
I used nls based on least squares to estimate, and therefore I cannot
assess the quality of models with AIC. nlme seems good for only mixed
models and mine is not mixed models.

res <- nls(y ~ d*(x)^3+a*(x)^2+b*x+c, start=list(a=2, b=1,c=1,d=1), data=d)

If anybody does know a R-function to estimate nonlinear model based on
maximum log likelihood, please let me know.

Thanks for your help in advance!
Odette

Odette Gaston <odette.gaston <at> gmail.com> writes:
> 
> Dear all,
> 
> Is it possible to generate AIC or something equivalent for nonlinear
> model estimated based on maximum log likelihood l in R?
> I used nls based on least squares to estimate, and therefore I cannot
> assess the quality of models with AIC. nlme seems good for only mixed
> models and mine is not mixed models.
> 
> res <- nls(y ~ d*(x)^3+a*(x)^2+b*x+c, start=list(a=2, b=1,c=1,d=1),
data=d)
> 
> If anybody does know a R-function to estimate nonlinear model based on
> maximum log likelihood, please let me know.
>
  AIC(res) should work just fine.
  Ordinary least-squares fitting is equivalent to assuming that the residuals
are independent and normally distributed with a homogeneous variance.  If
you're willing to make those assumptions you're set.  If not, there are
various options for relaxing them:  gnls in the nlme package for 
correlation and heteroscedasticity, mle (stats4) or mle2 (bbmle) for
normality.