R help - Nov 2019 - Global curve fitting/shared parameters with nls() alternatives

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

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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 <wagstaff.james at
gmail.com>
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]]
>
> ______________________________________________
> 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.
>
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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

On Tue, 5 Nov 2019 at 20:28, Bert Gunter <bgunter.4567 at gmail.com>
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 <wagstaff.james at
gmail.com>
> 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]]
>>
>> ______________________________________________
>> 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.
>>
>
-- 
James Wagstaff

+447910113349

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