Patrick Giraudoux <patrick.giraudoux <at> univ-fcomte.fr> writes:
> > Patrick Burns a écrit : > > Patrick Giraudoux wrote: > >> Bert Gunter a écrit : > >>> Based on a simple scatterplot of pourcma vs transat, a 4 parameter > >>> logistic > >>> looks like wild overfitting, and that may be the source of your > >>> problems. > >>> Given the huge scatter, a straight line is about as much as would seem > >>> sensible. I think this falls into the "Why ever would you want to do > >>> such a > >>> thing?" category. > >>> > >>> -- Bert > >>> > >> > >> Right, well, the general idea was just to show that the "straight > >> line" was the best model indeed (in the other data sets, with model > >> comparison, the logistic one was clearly shown to be the best... ). > >> Can the fact that convergence cannot be obtained be an acceptable and > >> sufficient reason to select the null model (the straight line) ? > > > > It is my experience that convergence problems are > > often encountered when the model makes little sense. > > I'm not so sure that non-convergence on its own is > > a good reason to reject the model. That is, to answer > > your specific question, I think it is acceptable but not > > sufficient. > > > > Patrick Burns > > patrick <at> burns-stat.com > > +44 (0)20 8525 0696 > > http://www.burns-stat.com > > (home of "The R Inferno" and "A Guide for the Unwilling S User") > > OK. Thanks for this opinion. Actually I was sharing it intuitively but > facing such situation for the first time, was quite unconfortable to > make a decision (and still I am). We are touching epistemology... and > maybe a bit far from purely technical thus from the R list issues. > A technical solution to this particular problem: with(bdd,plot(pourcma~transat)) stval <- list(Asym=30,xmid=0.07, scal=0.02) with(stval,curve(Asym/(1+exp((xmid-x)/scal)),add=TRUE)) nls(pourcma~SSlogis(transat, Asym, xmid, scal), start=c(Asym=30, xmid=0.07, scal=0.02),data=bdd, weights=sqrt(nbfeces),trace=T,alg="plinear") library(bbmle) m1 <- mle2(pourcma~dnorm(mean=Asym/(1+exp((xmid-transat)/scal)),sd=sd), start=c(stval,list(sd=0.1)),method="Nelder-Mead", data=bdd) with(as.list(coef(m1)),curve(Asym/(1+exp((xmid-x)/scal)),add=TRUE,col=2)) It happens to be able to find the flat-line solution (although it should really complain about lack of convergence, since the scale parameter should go to infinity and the midpoint parameter should be arbitrary in this case -- only Asym and the standard deviation are well defined). Ben Bolker ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
