John, have you ever looked at interval optimization as an alternative since it can lead to provably global minima?
Bernard Sent from my iPhone so please excuse the spelling!" > On May 13, 2020, at 8:42 AM, J C Nash <profjcn...@gmail.com> wrote: > > The Richards' curve is analytic, so nlsr::nlxb() should work better than > nls() for getting derivatives -- > the dreaded "singular gradient" error will likely stop nls(). Also likely, > since even a 3-parameter > logistic can suffer from it (my long-standing Hobbs weed infestation problem > below), is > that the Jacobian will be near-singular. And badly scaled. Nonlinear fitting > problems essentially > have different scale in different portions of the parameter space. > > You may also want to "fix" or mask one or more parameters to reduce the > dimensionality of the problem, > and nlsr::nlxb() can do that. > > The Hobbs problem has the following 12 data values for time points 1:12 > > # Data for Hobbs problem > ydat <- c(5.308, 7.24, 9.638, 12.866, 17.069, 23.192, 31.443, > 38.558, 50.156, 62.948, 75.995, 91.972) # for testing > tdat <- seq_along(ydat) # for testing > > An unscaled model is > > eunsc <- y ~ b1/(1+b2*exp(-b3*tt)) > > This problem looks simple, but has given lots of software grief over nearly 5 > decades. In 1974 an > extensive search had all commonly available software failing, which led to > the code that evolved > into nlsr, though there are plenty of cases where really awful code will > luckily find a good > solution. The issue is getting a solution and knowing it is reasonable. I > suspect a Richards' > model will be more difficult unless the OP has a lot of data and maybe some > external information > to fix or constrain some parameters. > > JN > > >> On 2020-05-13 5:41 a.m., Peter Dalgaard wrote: >> Shouldn't be hard to set up with nls(). (I kind of suspect that the Richards >> curve has more flexibility than data can resolve, especially the subset >> (Q,B,nu) seems highly related, but hey, it's your data...) >> >> -pd >> >>>> On 13 May 2020, at 11:26 , Christofer Bogaso <bogaso.christo...@gmail.com> >>>> wrote: >>> >>> Hi, >>> >>> Is there any R package to fit Richards' curve in the form of >>> https://en.wikipedia.org/wiki/Generalised_logistic_function >>> >>> I found there is one package grofit, but currently defunct. >>> >>> Any pointer appreciated. >>> >>> ______________________________________________ >>> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see >>> 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. >> > > ______________________________________________ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > 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. ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.
