Many moons ago (I think early 80s) I looked at some of the global optimizers, including several based on intervals. For problems of this size, your suggestion makes a lot of sense, though it has been so long since I looked at those techniques that I will avoid detailed comment.
I've not looked to see if there are any such solvers for R, but would be happy to learn (probably best off-list). Also I'm willing to work at a modest pace on developing one. A starting point might be nls2 package. Best, JN On 2020-05-13 11:05 a.m., Bernard Comcast wrote: > 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.
