[R] Does AIC() applied to a nls() object use the correct number of estimated parameters?

[Adaikalavan Ramasamy]

I do not know anything about nls(), so apologies if I get it completely
wrong. help("AIC") says that AIC is defined to be 
-2*log-likelihood + k*npar; where k = 2 by default. 

I think you calculated -2*log-likelihood + k*(npar + 1) instead. Does
this help ?

On Fri, 2004-07-16 at 03:50, Peter.Caley at csiro.au wrote:
> I'm wondering whether AIC scores extracted from nls() objects using
> AIC() are based on the correct number of estimated parameters.  
> 
> Using the example under nls() documentation:
> 
> > data( DNase )
> > DNase1 <- DNase[ DNase$Run == 1, ]
> > ## using a selfStart model
> > fm1DNase1 <- nls( density ~ SSlogis( log(conc), Asym, xmid, scal ),
> DNase1 )
> 
> Using AIC() function:
> 
> > AIC(fm1DNase1)
> [1] -78.41642
> 
> Using number of estimable coefficients (including residual error):
> 
> > -2*logLik(fm1DNase1) + 2*(length(coef(fm1DNase1))+1)
> [1] -76.41642
> attr(,"df")
> [1] 3
> attr(,"nall")
> [1] 16
> attr(,"nobs")
> [1] 16
> attr(,"class")
> [1] "logLik"
> 
> Based on the difference in AIC of 2 between the two approaches, it
> appears that when applied to a nls() object, AIC() doesn't include the
> estimate of residual error in the number of estimated parameters ... or
> is my understanding of nls() fitting confused. 
> 
> Any help appreciated.
> 
> cheers
> 
> Peter
> 
> *********************************************************************
> Dr Peter Caley
> CSIRO Entomology
> GPO Box 1700, Canberra,
> ACT 2601
> Email: peter.caley at csiro.au
> Ph: +61 (0)2 6246 4076   Fax: +61 (0)2 6246 4000
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