>>>>> James Wagstaff

>>>>> on Fri, 8 Nov 2019 13:20:41 +0000 writes:

> Dear Bert Thanks for getting back to me. Yes that is

> exactly the sort of problem I am trying to solve. I am

> aware of the option of hard coding the experimental groups

> as you suggested, but was hoping for an easy out of the

> box approach as I have many groups! Thanks James

If I understand correctly,

nlme :: nlsList() is exactly what you want.

No need to install anything, as 'nlme' is among the formally

'Recommended' packages and hence is part of every

(non-handicapped) R installation.

Best,

Martin Maechler

ETH Zurich and R Core Team

> On Tue, 5 Nov 2019 at 20:28, Bert Gunter

> <

[hidden email]> wrote:

>> A simplified example of what you wish to do might help to

>> clarify here.

>>

>> Here's my guess. Feel free to dismiss if I'm off base.

>>

>> Suppose your model is: y = exp(a*x) + b

>>

>> and you wish the b to be constant but the a to vary

>> across expts. Then can you not combine the data from both

>> into single x, y vectors, add a variable expt that takes

>> the value 1 for expt1 and 2 for expt 2 and fit the single

>> model:

>>

>> y = (expt ==1)*(exp(a1*x) + b) + (expt == 2)* (exp(a2*x)

>> + b)

>>

>> This would obtain separate estimates of a1 and a2 but a

>> single estimate of b .

>>

>> There are probably better ways to do this, but I've done

>> hardly any nonlinear model fitting (so warning!) and can

>> only offer this brute force approach; so wait for someone

>> to suggest something better before trying it.

>>

>> Cheers, Bert

>>

>>

>> On Tue, Nov 5, 2019 at 9:12 AM James Wagstaff

>> <

[hidden email]> wrote:

>>

>>> Hello I am trying to determine least-squares estimates

>>> of the parameters of a nonlinear model, where I expect

>>> some parameters to remain constant across experiments,

>>> and for others to vary. I believe this is typically

>>> referred to as global curve fitting, or the presence of

>>> shared/nested parameters. The "[]" syntax in the

>>> stats::nls() function is an extremely convenient

>>> solution (

>>>

>>>

https://r.789695.n4.nabble.com/How-to-do-global-curve-fitting-in-R-td4712052.html >>> ), but in my case I seem to need the

>>> Levenberg-Marquardt/Marquardt solvers such as

>>> nlsr::nlxb() and minpack.lm::nlsLM. I can not find any

>>> examples/documentation explaining a similar syntax for

>>> these tools. Is anyone aware of a nls-like tool with

>>> this functionality, or an alternative approach? Best

>>> wishes James Wagstaff

>>>

>>> [[alternative HTML version deleted]]

>>>

>>> ______________________________________________

>>>

[hidden email] 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.

>>>

>>

> --

> James Wagstaff

> +447910113349

> [[alternative HTML version deleted]]

> ______________________________________________

>

[hidden email] 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.

______________________________________________

[hidden email] mailing list -- To UNSUBSCRIBE and more, see

https://stat.ethz.ch/mailman/listinfo/r-helpPLEASE do read the posting guide

http://www.R-project.org/posting-guide.htmland provide commented, minimal, self-contained, reproducible code.