[R] Global curve fitting/shared parameters with nls() alternatives

[Martin Maechler]

>>>>> 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
    > <bgunter.4567 using 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 using 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]]
    >>> 
    >>> ______________________________________________
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    >>> 
    >> 

    > -- 
    > James Wagstaff

    > +447910113349

    > 	[[alternative HTML version deleted]]

    > ______________________________________________
    > R-help using 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.