[R] Fitting Richards' curve

Bernard Comcast mcg@rvey@bern@rd @end|ng |rom comc@@t@net
Wed May 13 17:15:50 CEST 2020


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 using 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 using 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.
>>> 
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>> 
> 
> ______________________________________________
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> and provide commented, minimal, self-contained, reproducible code.



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