Peter.Caley@csiro.au

2004-Jul-16 02:50 UTC

### [R] 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

Adaikalavan Ramasamy

2004-Jul-16 03:13 UTC

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

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 > > ______________________________________________ > R-help at stat.math.ethz.ch mailing list > https://www.stat.math.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html >

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