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: [EMAIL PROTECTED] Ph: +61 (0)2 6246 4076 Fax: +61 (0)2 6246 4000 ______________________________________________ [EMAIL PROTECTED] 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
