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

[Peter.Caley@csiro.au]

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