R help - Jul 2004 - Does AIC() applied to a nls() object use the correct number of estimated parameters?

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

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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