Re: [R] Fitting Richards' curve
I have been using nlsr() to fit s curves to Covid-19 data over the past few weeks and I have not had any issues. Bernard Sent from my iPhone so please excuse the spelling!" > On May 13, 2020, at 5:16 PM, Abby Spurdle wrote: > > Hi Christofer, > > This doesn't really answer your question. > But if the goal is to fit an S-shaped curve to data, with increased > flexibility... > (I'm assuming that's the goal). > > ...then I'd like to note the option of splines (or smoothing), subject > to shape constraints... > > My guess, is it's probably easier to model the inverse of a growth > curve this way, than to model the growth curve directly. > In which case, a 4-piece to 10-piece spline should give considerably flexibly. > > It's possible that Martin's package, cobs, can do this, but not sure, > I haven't tried it. > And there may be other R packages for fitting splines/smoothers to > data, subject to shape constraints. > > If not, I'm guessing it wouldn't be too difficult to implement, via > extensions to the quadprog package, for quadratic programming. > > >> On Wed, May 13, 2020 at 9:26 PM Christofer Bogaso >> 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.
Re: [R] Fitting Richards' curve
> It's possible that Martin's package, cobs, can do this, but not sure, > I haven't tried it. > And there may be other R packages for fitting splines/smoothers to > data, subject to shape constraints. Further to my previous post. I read through the documentation for the cobs package. And (someone please correct me if I'm wrong) I don't think it can produce a smoother with an S-shaped shape constraint. I found two other packages for smoothing under shape constraints, but doesn't look like they can either. I have code that could be adapted for this purpose. But one it's incomplete, two it (currently) requires the user to specify a smoothness parameter, and three doesn't produce any kind of inference... __ 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.
Re: [R] Fitting Richards' curve
Hi Christofer, This doesn't really answer your question. But if the goal is to fit an S-shaped curve to data, with increased flexibility... (I'm assuming that's the goal). ...then I'd like to note the option of splines (or smoothing), subject to shape constraints... My guess, is it's probably easier to model the inverse of a growth curve this way, than to model the growth curve directly. In which case, a 4-piece to 10-piece spline should give considerably flexibly. It's possible that Martin's package, cobs, can do this, but not sure, I haven't tried it. And there may be other R packages for fitting splines/smoothers to data, subject to shape constraints. If not, I'm guessing it wouldn't be too difficult to implement, via extensions to the quadprog package, for quadratic programming. On Wed, May 13, 2020 at 9:26 PM Christofer Bogaso 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.
Re: [R] Fitting Richards' curve
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 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 > 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.
Re: [R] Fitting Richards' curve
Also, in the full curve referenced on Wikpedia, the parameters Q And M are confounded - you only need one or the other But not both. If you are using both and trying to estimate them both you will have problems. I have fitted these curves quite easily using the Solver in Excel. Bernard Sent from my iPhone so please excuse the spelling!" > On May 13, 2020, at 8:42 AM, J C Nash 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 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.
Re: [R] Fitting Richards' curve
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 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 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.
Re: [R] Fitting Richards' curve
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 >> 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.
Re: [R] Fitting Richards' curve
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 > 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. -- Peter Dalgaard, Professor, Center for Statistics, Copenhagen Business School Solbjerg Plads 3, 2000 Frederiksberg, Denmark Phone: (+45)38153501 Office: A 4.23 Email: pd@cbs.dk Priv: pda...@gmail.com __ 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.
Re: [R] Fitting Richards' curve
Hi Christofer Try FlexParamCurve or maybe drc package. Cheers Petr > -Original Message- > From: R-help On Behalf Of Christofer Bogaso > Sent: Wednesday, May 13, 2020 11:26 AM > To: r-help > Subject: [R] Fitting Richards' curve > > 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.
