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? 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.