Hi Peter, thank you for your reply. Now I see, that P3 is indeed redundand. But with the simplified model...
fit = nls(yeps ~ p1 / (1 + exp(p2 - x)) * exp(p4 * x)) ...nls still produces the same error. Any ideas? Felix 2011/4/12 Peter Ehlers <ehl...@ucalgary.ca> > On 2011-04-11 13:29, Felix Nensa wrote: > >> Hi, >> >> I am using nls to fit a non linear function to some data but R keeps >> giving >> me "singular gradient matrix at initial parameter estimates" errors. >> For testing purposes I am doing this: >> >> ### R code ### >> >> x<- 0:140 >> y<- 200 / (1 + exp(17 - x)/2) * exp(-0.02*x) # creating 'perfect' samples >> with fitting model >> yeps<- y + rnorm(length(y), sd = 2) # adding noise >> >> # results in above error >> fit = nls(yeps ~ p1 / (1 + exp(p2 - x) / p3) * exp(p4 * x)) >> >> ### >> >> From what I've found in this list I think that my model is >>> over-parameterized. >>> >> How can I work around that? >> > > Take out p3; it's redundant. > > Peter Ehlers > > Thanks, >> >> Felix >> >> [[alternative HTML version deleted]] >> >> ______________________________________________ >> 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. >> > > [[alternative HTML version deleted]] ______________________________________________ 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.
