Peter.Caley@csiro.au
2004-Jul-16 03:57 UTC
[R] Does AIC() applied to a nls() object use the correctnumber of estimated parameters?
Thanks Adaikalavan, however the problem remains. Considering AIC() as applied to the linear model in AIC() help documentation:> data(swiss) > lm1 <- lm(Fertility ~ . , data = swiss) > AIC(lm1)[1] 326.0716 Clearly this includes the estimation of the residual standard error as an estimated parameter, as this gives the correct score:> -2*logLik(lm1) + 2*(length(coef(lm1))+1)[1] 326.0716 attr(,"nall") [1] 47 attr(,"nobs") [1] 47 attr(,"df") [1] 7 attr(,"class") [1] "logLik" I thought the same would have held for nls() objects.> -----Original Message----- > From: Adaikalavan Ramasamy [mailto:ramasamy at cancer.org.uk] > Sent: Friday, 16 July 2004 1:14 PM > To: Caley, Peter (Entomology, Black Mountain) > Cc: R-help > Subject: Re: [R] Does AIC() applied to a nls() object use the > correctnumber 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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