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.
Fitting Richards' curve
10 messages · Christofer Bogaso, PIKAL Petr, Peter Dalgaard +3 more
Hi Christofer Try FlexParamCurve or maybe drc package. Cheers Petr
-----Original Message----- From: R-help <r-help-bounces at r-project.org> On Behalf Of Christofer Bogaso Sent: Wednesday, May 13, 2020 11:26 AM To: r-help <r-help at r-project.org> 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.
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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.christofer at 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 at 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.mes at cbs.dk Priv: PDalgd at gmail.com
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.christofer at 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 at 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.
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 <profjcnash at 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.christofer at 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 at 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 at 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.
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 <profjcnash at 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.christofer at 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 at 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 at 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.
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 <profjcnash at 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.christofer at 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 at 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 at 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.
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
<bogaso.christofer at 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 at 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.
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...
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 <spurdle.a at gmail.com> 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 <bogaso.christofer at 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 at 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 at 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.